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		<id>https://www.na-mic.org/w/index.php?title=2014_Summer_Project_Week&amp;diff=85960</id>
		<title>2014 Summer Project Week</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=2014_Summer_Project_Week&amp;diff=85960"/>
		<updated>2014-06-19T16:16:28Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Head and Neck Cancer */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;__NOTOC__&lt;br /&gt;
&lt;br /&gt;
[[image:PW-MIT2014.png|300px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Dates: June 23-27, 2014.&lt;br /&gt;
&lt;br /&gt;
Location: MIT, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Agenda==&lt;br /&gt;
&lt;br /&gt;
{|border=&amp;quot;1&amp;quot;&lt;br /&gt;
|-style=&amp;quot;background:#b0d5e6;color:#02186f&amp;quot; &lt;br /&gt;
!style=&amp;quot;width:10%&amp;quot; |Time&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Monday, June 23&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Tuesday, June 24&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Wednesday, June 25&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Thursday, June 26&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Friday, June 27&lt;br /&gt;
|-&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#dbdbdb&amp;quot;|'''Project Presentations'''&lt;br /&gt;
|bgcolor=&amp;quot;#6494ec&amp;quot;|'''NA-MIC Update Day'''&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#88aaae&amp;quot;|'''IGT Day'''&lt;br /&gt;
|bgcolor=&amp;quot;#faedb6&amp;quot;|'''Reporting Day'''&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''8:30am'''&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''9am-12pm'''&lt;br /&gt;
|&lt;br /&gt;
|'''10-11:30pm''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: DICOM|DICOM]] (Steve Pieper)&lt;br /&gt;
[[MIT_Project_Week_Rooms|Grier Room (Left)]] &lt;br /&gt;
|&lt;br /&gt;
|&lt;br /&gt;
'''9:00-10:30am''' [[2014_Tutorial_Contest|Tutorial Contest Presentations (Sonia Pujol)]] &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''10am-12pm: &amp;lt;font color=&amp;quot;#4020ff&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: IGT Neuro|Image-Guided Therapy - Neurosurgery]] (Alexandra Golby, Tina Kapur) &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Star|Star]]&lt;br /&gt;
|'''10am-12pm:''' [[#Projects|Project Progress Updates]] &amp;lt;br&amp;gt;&lt;br /&gt;
'''12pm''' [[Events:TutorialContestJune2014|Tutorial Contest Winner Announcement]]&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''12pm-1pm'''&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch &lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch boxes; Adjourn by 1:30pm&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''1pm-5:30pm'''&lt;br /&gt;
|'''1-1:05pm: &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Ron Kikinis: Welcome&amp;lt;/font&amp;gt;'''&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''3:30-4:30pm''' [[2014 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin)  &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]&lt;br /&gt;
|'''1-3pm:''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: QIICR|QIICR]] (Andrey Fedorov)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Kiva|Kiva]] &lt;br /&gt;
|'''1-2:30pm:''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: Contours|Contours]] (Adam Rankin, Csaba Pinter)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Kiva|Kiva]] &lt;br /&gt;
|'''1-3pm:''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: IGT Prostate|Image-Guided Therapy - Prostate Interventions]] (Clare Tempany, Noby Hata)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Star|Star]] &lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''5:30pm'''&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
== '''Background''' ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.   &lt;br /&gt;
&lt;br /&gt;
Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event.  Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.&lt;br /&gt;
&lt;br /&gt;
The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools.  The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical.  To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects.  On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.&lt;br /&gt;
&lt;br /&gt;
A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]&lt;br /&gt;
&lt;br /&gt;
=Projects=&lt;br /&gt;
* [[2014_Project_Week_Template | Template for project pages]]&lt;br /&gt;
&lt;br /&gt;
==TBI==&lt;br /&gt;
&lt;br /&gt;
==Atrial Fibrillation==&lt;br /&gt;
&lt;br /&gt;
==Huntington's Disease==&lt;br /&gt;
&lt;br /&gt;
==Head and Neck Cancer==&lt;br /&gt;
*[[2014_Summer_Project_Week:Interactive_DIR| Interactive DIR]] (Greg Sharp, Ivan Kolesov, Allen Tannenbaum)&lt;br /&gt;
*[[2014_Summer_Project_Week:DIR_validation_tools| DIR validation tools]] (Greg Sharp, Ivan Kolesov, Allen Tannenbaum)&lt;br /&gt;
*[[2014_Summer_Project_Week:Upload_HN_data| Upload H&amp;amp;N data]] (Greg Sharp, Paolo Zaffino)&lt;br /&gt;
*[[2014_Summer_Project_Week:DIR_stop_and_restart| DIR stop and restart]] (Paolo Zaffino, Greg Sharp)&lt;br /&gt;
&lt;br /&gt;
==Slicer4 Extensions==&lt;br /&gt;
&lt;br /&gt;
*[[2014_Summer_Project_Week:Multidim Data| Multidim Data]] (Kevin Wang, Andras, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:DICOM-SRO import| DICOM-SRO import]] (Kevin Wang)&lt;br /&gt;
*[[2014_Summer_Project_Week:PLM_engineering| Plastimatch extension re-engineering]] (Greg Sharp, Paolo Zaffino, Andras, Csaba, Kevin)&lt;br /&gt;
&lt;br /&gt;
==Cardiac==&lt;br /&gt;
&lt;br /&gt;
==Stroke==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Brain Segmentation==&lt;br /&gt;
&lt;br /&gt;
==Image-Guided Therapy==&lt;br /&gt;
&lt;br /&gt;
* SlicerIGT extension: testing, tutorials, website (Tamas Ungi, Nobuhiko Hata)&lt;br /&gt;
* [[Gestural Point of Care Interface for IGT]] (Saskia, Franklin, Steve, Tobias)&lt;br /&gt;
*[[2014_Summer_Project_Week:MR-Ultrasound_Registration_for_Prostate_Interventions | MR-Ultrasound Registration for Prostate Interventions]] (Chenxi Zhang, Andriy Fedorov, Andras)&lt;br /&gt;
*[[2014_Summer_Project_Week:Surface_approximation_from_contour_points | Surface approximation from contour points]] (Chenxi Zhang, Csaba Pinter, Andrey Fedorov)&lt;br /&gt;
* Steered image registration using intelligent interfaces for minimal user interaction (Marcel Prastawa, Jim Miller, Steve Pieper)&lt;br /&gt;
* [[2014_Summer_Project_Week:Image To Mesh Conversion for Brain MRI | Image To Mesh Conversion for Brain MRI]] (Fotis Drakopoulos, Yixun Liu, Andrey Fedorov, Ron Kikinis, Nikos Chrisochoides)&lt;br /&gt;
* [[2014_Summer_Project_Week:An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift | An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift]] (Fotis Drakopoulos, Yixun Liu, Andriy Kot, Andrey Fedorov, Olivier Clatz, Ron Kikinis, Nikos Chrisochoides)&lt;br /&gt;
* [[2014_Summer_Project_Week:Robot_Control_With_OpenIGTLink | Robot Control With OpenIGTLink]]   ( Gregory Fischer(WPI), Nirav Patel(WPI), Nobuhiko Hata (BWH) )&lt;br /&gt;
* [[2014_Summer_Project_Week:Open_source_electromagnetic_trackers_usingOpenIGTLink| Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)&lt;br /&gt;
&lt;br /&gt;
==Radiation Therapy==&lt;br /&gt;
*[[2014_Summer_Project_Week:External Beam Planning| External Beam Planning]] (Kevin Wang, Greg Sharp, Maxime Desplanques, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Proton_pencil_beam| Proton pencil beam dose calculation]] (Maxime Desplanques, Kevin Wang, Greg Sharp)&lt;br /&gt;
&lt;br /&gt;
==Chronic Obstructive Pulmonary Disease ==&lt;br /&gt;
&lt;br /&gt;
==[http://qiicr.org QIICR]==&lt;br /&gt;
* [[2014_Summer_Project_Week: RWV mapping support|Real world value mapping support]] (Andrey, Ethan, Andras, Steve, Jim, ...)&lt;br /&gt;
* [[2014_Summer_Project_Week: CLI Derived DICOM Data| Proper formatting of DICOM Derived Data from CLI]] (Steve, Andrey, Jim, {Michael and David remotely})&lt;br /&gt;
&lt;br /&gt;
==Infrastructure==&lt;br /&gt;
*Slicer 4.4 Release (JC, Steve, Nicole)&lt;br /&gt;
* [[2014_Summer_Project_Week: Chronicle| Chronicle]] (Steve)&lt;br /&gt;
* [[2014_Summer_Project_Week: Volume Registration|Volume Registration]] (Steve, Greg, Marcel, Jim)&lt;br /&gt;
* [[2014_Summer_Project_Week:Markups | Markups]] (Nicole Aucoin)&lt;br /&gt;
*[[2014_Summer_Project_Week:Pluggable Label Statistics |Pluggable Label Statistics]] (Andrey , Ethan, Steve, Brad, Jim? Dirk?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Subject_hierarchy_integration | Subject hierarchy integration]] (Csaba, Steve, Jc, Andras?, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Contours | Contours]] (Adam Rankin, Csaba, Andras, Steve, Jc, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Parameter Node Serialization | Parameter Node Serialization]] (Kevin Wang, Andras, Steve, Jim, Csaba, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Self-tests for non-linear transforms | Self-tests for non-linear transforms]] (Xining Du)&lt;br /&gt;
&lt;br /&gt;
==Feature Extraction==&lt;br /&gt;
*Breast Tumor Segmentation and Heterogeneity Analysis (Vivek Narayan, Jay Jagadeesan)&lt;br /&gt;
*Quantitative image feature extraction in Non-Small Cell Lung Cancer  (Hugo Aerts)&lt;br /&gt;
*[[2014_Summer_Project_Week:Invariant_Feature_Extraction_Slicer | Invariant Feature Methods in Slicer]] (Matthew Toews, Nicole Aucoin, Sandy Wells)&lt;br /&gt;
&lt;br /&gt;
==Other==&lt;br /&gt;
*[[2014_Summer_Project_Week:Slicer_Murin_Shape_Analysis | Shape Analysis for the developing murine skull]] (Murat Maga, Ryan Young, Seattle Chidren's Hospital).&lt;br /&gt;
*[[2014_Summer_Project_Week:Slicer_LDDMM_Shape_Analysis | Slicer Interface to LDDMM shape anlaysis]] (Saurabh Jain, JHU; Steve Pieper, Isomics; Josh Cates, SCI, Utah; Hans Johnson, Iowa; Martin Styner, UNC)&lt;br /&gt;
*[[2014_Summer_Project_Week:Image_Registration_with_Sliding_Motion_Constraints | Image Registration with Sliding Motion Constraints]] (Alexander Derksen, Kanglin Chen, Gregory Sharp, Danielle Pace?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Atlas Selection | Atlas Selection]] (Kanglin Chen, Gregory Sharp)&lt;br /&gt;
*[[2014_Summer_Project_Week:Multiscale_Non_Local_Means_filter_(NLM)_for_chest_CT_images | Multiscale Non Local Means filter (NLM) for chest CT images]] (Pietro Nardelli, University College Cork (UCC), Ireland)&lt;br /&gt;
*[[2014_Summer_Project_Week:Intraoperative_Registration_of_preoperative_CT_and_C-arm_CT_of_the_lung | Intraoperative Registration of preoperative CT and C-arm CT of the lung]] (Katharina Breininger, Jay Jagadeesan)&lt;br /&gt;
*[[2014_Summer_Project_Week:CAD_Toolbox_for_Neurological_Disorders | CAD Toolbox for Neurological Disorders]] (Sidong Liu, Siqi Liu, Fan Zhang, {Yang Song, Weidong Cai}, Sonia Pujol, Ron Kikinis {remotely})&lt;br /&gt;
&lt;br /&gt;
== '''Logistics''' ==&lt;br /&gt;
&lt;br /&gt;
*'''Dates:''' June 23-27, 2014.&lt;br /&gt;
*'''Location:''' [[MIT_Project_Week_Rooms| Stata Center / RLE MIT]]. &lt;br /&gt;
*'''REGISTRATION:''' https://www.regonline.com/namic2014summerprojectweek. Please note that  as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on &amp;quot;No thanks&amp;quot; in order to finish your Project Week registration.&lt;br /&gt;
*'''Registration Fee:''' $300.&lt;br /&gt;
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.&lt;br /&gt;
*'''Room sharing''': If interested, add your name to the list:  [[2014_Summer_Project_Week/RoomSharing|here]]&lt;br /&gt;
&lt;br /&gt;
== '''Registrants''' ==&lt;br /&gt;
&lt;br /&gt;
Do not add your name to this list - it is maintained by the organizers based on your paid registration.  ([https://www.regonline.com/namic2014summerprojectweek  Please click here to register.])&lt;br /&gt;
&lt;br /&gt;
#Hugo Aerts, Dana Farber/Harvard, hugo_aerts@dfci.harvard.edu&lt;br /&gt;
#Peter Anderson, retired, traneus@verizon.net&lt;br /&gt;
#Nicole Aucoin, Brigham &amp;amp; Women's Hospital, nicole@bwh.harvard.edu&lt;br /&gt;
#Eva Breininger, Brigham &amp;amp; Women's Hospital, ebreininger@partners.org&lt;br /&gt;
#Francois Budin, NIRAL-UNC, fbudin@unc.edu&lt;br /&gt;
#Saskia Camps, SPL, saskiacamps@gmail.com&lt;br /&gt;
#Lucia Cevidanes, University of Michigan, luciacev@umich.edu&lt;br /&gt;
#Laurent Chauvin, SPL, lchauvin@bwh.harvard.edu&lt;br /&gt;
#Kanglin Chen, Fraunhofer MEVIS, kanglin.chen@mevis.fraunhofer.de&lt;br /&gt;
#Adrian Dalca, MIT CSAIL, adalca@mit.edu&lt;br /&gt;
#Alexander Derksen, Fraunhofer MEVIS, alexander.derksen@mevis.fraunhofer.de&lt;br /&gt;
#Maxime Desplanques, MGH/Politecnico di Milano, maxime.desplanques@cnao.it&lt;br /&gt;
#Fotis Drakopoulos, Old Dominion University, fdrakopo@gmail.com&lt;br /&gt;
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu&lt;br /&gt;
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com&lt;br /&gt;
#James Fishbaugh, SCI Institute/University of Utah, jfishbaugh@gmail.com&lt;br /&gt;
#Polina Golland, MIT CSAIL, polina@csail.mit.edu&lt;br /&gt;
#Nobuhiko Hata, Brigham &amp;amp; Women's Hospital, hata@bwh.harvard.edu&lt;br /&gt;
#Saurabh Jain, Johns Hopkins University, saurabh@cis.jhu.edu&lt;br /&gt;
#Hans Johnson, University of Iowa, hans-johnson@uiowa.edu&lt;br /&gt;
#Jayashree Kalpathy-Cramer, MGH, kalpathy@nmr.mgh.harvard.edu&lt;br /&gt;
#Tina Kapur, BWH/Harvard Medical School, tkapur@bwh.harvard.edu&lt;br /&gt;
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu&lt;br /&gt;
#Franklin King, Queen's University, franklin.king@queensu.ca&lt;br /&gt;
#Tassilo Klein, SPL/BWH, TJKlein@bwh.harvard.edu&lt;br /&gt;
#Farukh Kohistani, BWH Radiology, kohistan@bc.edu&lt;br /&gt;
#Yangming Li, University of Washington, ymli81@uw.edu&lt;br /&gt;
#Sidong Liu, SPL/BWH, sliu@bwh.harvard.edu&lt;br /&gt;
#Siqi Liu, University of Sydney, sliu4512@uni.sydney.edu.au&lt;br /&gt;
#Bradley Lowekamp, National Institutes of Health, blowekamp@mail.nih.gov&lt;br /&gt;
#Murat Maga, Seattle Children's Research Institute, maga@uw.edu&lt;br /&gt;
#Katie Mastrogiacomo, SPL/BWH, kmast@bwh.harvard.edu&lt;br /&gt;
#Alireza Mehrtash, SPL/BWH, mehrtash@bwh.harvard.edu&lt;br /&gt;
#Dominik Meier, Brigham &amp;amp; Women's Hospital, meier@bwh.harvard.edu&lt;br /&gt;
#Jim Miller, GE Research, millerjv@ge.com&lt;br /&gt;
#Luiz Otavio Murta Junor, SPL/BWH, lmurta@partners.org&lt;br /&gt;
#Vivek Narayan, NCIGT, narayan.vivek9@gmail.com&lt;br /&gt;
#Pietro Nardelli, University College Cork, pietro@bwh.harvard.edu&lt;br /&gt;
#Yangming Ou, MGH, yangming.ou@uphs.upenn.edu&lt;br /&gt;
#Danielle Pace, MIT CSAIL, dfpace@mit.edu&lt;br /&gt;
#Keryn Palmer, Brigham &amp;amp; Women's Hospital, kpalmer5@partners.org&lt;br /&gt;
#Nirav Patel, WPI, napatel@wpi.edu&lt;br /&gt;
#Tobias Penzkofer, SPL, pt@bwh.harvard.edu&lt;br /&gt;
#Steve Pieper, Isomics Inc, pieper@isomics.com&lt;br /&gt;
#Csaba Pinter, Queen's University, csaba.pinter@queensu.ca&lt;br /&gt;
#Marcel Prastawa, GE Research, marcel.prastawa@ge.com&lt;br /&gt;
#Somia Pujol, Harvard Medical School, spujol@bwh.harvard.edu&lt;br /&gt;
#Adam Rankin, Queen's University, rankin@queensu.ca&lt;br /&gt;
#Aymeric Reshef, Brigham &amp;amp; Women's Hospital, areshef@bwh.harvard.edu&lt;br /&gt;
#Peter Savadjiev, Brigham &amp;amp; Women's Hospital, petersv@bwh.harvard.edu&lt;br /&gt;
#Gregory Sharp, MGH, gcsharp@mgh.harvard.edu&lt;br /&gt;
#Ramesh Sridharan, MIT CSAIL, rameshvs@csail.mit.edu&lt;br /&gt;
#Matthew Toews, BWH/Harvard Medical School, mt@bwh.harvard.edu&lt;br /&gt;
#Ethan Ulrich, University of Iowa, ethan-ulrich@uiowa.edu&lt;br /&gt;
#Tamas Ungi, Queen's University, ungi@queensu.ca&lt;br /&gt;
#David Welch, University of Iowa, david-welch@uiowa.edu&lt;br /&gt;
#William Wells, Brigham &amp;amp; Women's Hospital, sw@bwh.harvard.edu&lt;br /&gt;
#Alex Yarmarkovich, ISOMICS Inc., alexy@bwh.harvard.edu&lt;br /&gt;
#Ryan Young, Seattle Children's Research Institute, ryan.young@seattlechildrens.org&lt;br /&gt;
#Paolo Zaffino, University Magna Graecia of Catanzaro, p.zaffino@unicz.it&lt;br /&gt;
#Chenxi Zhang, Brigham &amp;amp; Women's Hospital, chenxizhang@fudan.edu.cn&lt;br /&gt;
#Fan Zhang, University of Sydney, fzha8048@uni.sydney.edu.au&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=2014_Summer_Project_Week&amp;diff=85625</id>
		<title>2014 Summer Project Week</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=2014_Summer_Project_Week&amp;diff=85625"/>
		<updated>2014-06-04T13:32:55Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Head and Neck Cancer */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;__NOTOC__&lt;br /&gt;
&lt;br /&gt;
[[image:PW-MIT2014.png|300px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Dates: June 23-27, 2014.&lt;br /&gt;
&lt;br /&gt;
Location: MIT, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Agenda==&lt;br /&gt;
&lt;br /&gt;
{|border=&amp;quot;1&amp;quot;&lt;br /&gt;
|-style=&amp;quot;background:#b0d5e6;color:#02186f&amp;quot; &lt;br /&gt;
!style=&amp;quot;width:10%&amp;quot; |Time&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Monday, June 23&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Tuesday, June 24&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Wednesday, June 25&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Thursday, June 26&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Friday, June 27&lt;br /&gt;
|-&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#dbdbdb&amp;quot;|'''Project Presentations'''&lt;br /&gt;
|bgcolor=&amp;quot;#6494ec&amp;quot;|'''NA-MIC Update Day'''&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#88aaae&amp;quot;|'''IGT and RT Day'''&lt;br /&gt;
|bgcolor=&amp;quot;#faedb6&amp;quot;|'''Reporting Day'''&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''8:30am'''&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''9am-12pm'''&lt;br /&gt;
|&lt;br /&gt;
|'''10-11:30pm''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: DICOM|DICOM]] (Steve Pieper)&lt;br /&gt;
[[MIT_Project_Week_Rooms|Grier Room (Left)]] &lt;br /&gt;
|'''9:30-11am: &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session:Registration Algorithms|Registration Algorithms]]'''(Sandy Wells) &lt;br /&gt;
[[MIT_Project_Week_Rooms#Star|Star]]&lt;br /&gt;
|&lt;br /&gt;
'''9:30-10:30am''' [[2014_Tutorial_Contest|Tutorial Contest Presentations (Sonia Pujol)]] &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''10am-12pm: &amp;lt;font color=&amp;quot;#4020ff&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: IGT|Image-Guided Therapy - Neurosurgery]] (Alexandra Golby, Tina Kapur) &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Star|Star]]&lt;br /&gt;
|'''10am-12pm:''' [[#Projects|Project Progress Updates]] &amp;lt;br&amp;gt;&lt;br /&gt;
'''12pm''' [[Events:TutorialContestJune2014|Tutorial Contest Winner Announcement]]&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''12pm-1pm'''&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch &lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch boxes; Adjourn by 1:30pm&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''1pm-5:30pm'''&lt;br /&gt;
|'''1-1:05pm: &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Ron Kikinis: Welcome&amp;lt;/font&amp;gt;'''&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''3:30-4:30pm''' [[2014 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin)  &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]&lt;br /&gt;
|'''1-3pm:''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: QIICR|QIICR]] (Andrey Fedorov)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Kiva|Kiva]] &lt;br /&gt;
|'''1-2:30pm:''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: Contours|Contours]] (Adam Rankin, Csaba Pinter)&lt;br /&gt;
TBD [[MIT_Project_Week_Rooms#Kiva|Kiva]] &lt;br /&gt;
|'''1-3pm:''' &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2014 Project Week Breakout Session: IGT|Image-Guided Therapy - Prostate Interventions]] (Clare Tempany, Tina Kapur)&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''3-5:30pm: &amp;lt;font color=&amp;quot;#4020ff&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;TBD&lt;br /&gt;
[[MIT_Project_Week_Rooms#Star|Star]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''5:30pm'''&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
== '''Background''' ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.   &lt;br /&gt;
&lt;br /&gt;
Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event.  Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.&lt;br /&gt;
&lt;br /&gt;
The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools.  The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical.  To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects.  On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.&lt;br /&gt;
&lt;br /&gt;
A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]&lt;br /&gt;
&lt;br /&gt;
=Projects=&lt;br /&gt;
* [[2014_Project_Week_Template | Template for project pages]]&lt;br /&gt;
&lt;br /&gt;
==TBI==&lt;br /&gt;
&lt;br /&gt;
==Atrial Fibrillation==&lt;br /&gt;
&lt;br /&gt;
==Huntington's Disease==&lt;br /&gt;
&lt;br /&gt;
==Head and Neck Cancer==&lt;br /&gt;
*[[2014_Summer_Project_Week:Interactive_DIR| Interactive DIR]] (Greg, Ivan)&lt;br /&gt;
*[[2014_Summer_Project_Week:DIR_validation_tools| DIR validation tools]] (Greg, Ivan)&lt;br /&gt;
*[[2014_Summer_Project_Week:Upload_HN_data| Upload H&amp;amp;N data]] (Greg, Paolo)&lt;br /&gt;
&lt;br /&gt;
==Slicer4 Extensions==&lt;br /&gt;
&lt;br /&gt;
*[[2014_Summer_Project_Week:Multidim Data| Multidim Data]] (Kevin Wang, Andras, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:DICOM-SRO import| DICOM-SRO import]] (Kevin Wang)&lt;br /&gt;
&lt;br /&gt;
==Cardiac==&lt;br /&gt;
&lt;br /&gt;
==Stroke==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Brain Segmentation==&lt;br /&gt;
&lt;br /&gt;
==Image-Guided Therapy==&lt;br /&gt;
&lt;br /&gt;
* SlicerIGT extension: testing, tutorials, website (Tamas Ungi, Junichi Tokuda)&lt;br /&gt;
* Gestural Point of Care Interface for IGT (Saskia, Franklin, Tobias)&lt;br /&gt;
* MR-Ultrasound Registration for Prostate Interventions (Chenxi Zhang, Andriy Fedorov, Andras)&lt;br /&gt;
* Surface approximation from contour points (Chenxi Zhang, Csaba Pinter, Andrey Fedorov)&lt;br /&gt;
* Steered image registration using intelligent interfaces for minimal user interaction (Marcel Prastawa, Jim Miller, Steve Pieper)&lt;br /&gt;
* Image To Mesh Conversion for Brain MRI (Fotis Drakopoulos, Yixun Liu, Andrey Fedorov, Ron Kikinis, Nikos Chrisochoides)&lt;br /&gt;
* An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift (Fotis Drakopoulos, Yixun Liu, Andriy Kot, Andrey Fedorov, Olivier Clatz, Ron Kikinis, Nikos Chrisochoides)&lt;br /&gt;
&lt;br /&gt;
==Radiation Therapy==&lt;br /&gt;
*[[2014_Summer_Project_Week:External Beam Planning| External Beam Planning]] (Kevin Wang, Greg, ?)&lt;br /&gt;
&lt;br /&gt;
==TMJ-OA==&lt;br /&gt;
&lt;br /&gt;
==Chronic Obstructive Pulmonary Disease ==&lt;br /&gt;
&lt;br /&gt;
==[http://qiicr.org QIICR]==&lt;br /&gt;
* Real world value mapping support (Andrey, Andras, Steve, Jim, ...)&lt;br /&gt;
* Segmentation object support (Andrey, Csaba, Steve, ...)&lt;br /&gt;
&lt;br /&gt;
==Infrastructure==&lt;br /&gt;
*Slicer 4.4 Release (JC, Steve, Nicole)&lt;br /&gt;
*Chronicle (Steve Pieper)&lt;br /&gt;
*Volume Registration (Steve Pieper)&lt;br /&gt;
*OpenCL (Steve Pieper, Marcel Prastawa)&lt;br /&gt;
*Markups (Nicole Aucoin)&lt;br /&gt;
*Pluggable Label Statistics (Andrey , Ethan, Steve, Brad?, Jim? Dirk?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Subject_hierarchy_integration | Subject hierarchy integration]] (Csaba, Steve, Jc, Andras?, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Contours | Contours]] (Adam Rankin, Csaba, Andras, Steve, Jc, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Parameter Node Serialization | Parameter Node Serialization]] (Kevin Wang, Andras, Steve, Jim, Matt, Csaba, ?)&lt;br /&gt;
*[[2014_Summer_Project_Week:Self-tests for non-linear transforms | Self-tests for non-linear transforms]] (Xining Du)&lt;br /&gt;
&lt;br /&gt;
==Feature Extraction==&lt;br /&gt;
*Breast Tumor Segmentation and Heterogeneity Analysis (Vivek Narayan, Jay Jagadeesan)&lt;br /&gt;
*Quantitative image feature extraction in Non-Small Cell Lung Cancer  (Hugo Aerts)&lt;br /&gt;
&lt;br /&gt;
==Other==&lt;br /&gt;
*[[2014_Summer_Project_Week:Slicer_Murin_Shape_Analysis | Shape Analysis for the developing murine skull]] (Murat Maga, Ryan Young, Seattle Chidren's Hospital).&lt;br /&gt;
*[[2014_Summer_Project_Week:Slicer_LDDMM_Shape_Analysis | Slicer Interface to LDDMM shape anlaysis]] (Saurabh Jain, JHU; Steve Pieper, Isomics; Josh Cates, SCI, Utah; Hans Johnson, Iowa; Martin Styner, UNC)&lt;br /&gt;
&lt;br /&gt;
== '''Logistics''' ==&lt;br /&gt;
&lt;br /&gt;
*'''Dates:''' June 23-27, 2014.&lt;br /&gt;
*'''Location:''' [[MIT_Project_Week_Rooms| Stata Center / RLE MIT]]. &lt;br /&gt;
*'''REGISTRATION:''' https://www.regonline.com/namic2014summerprojectweek. Please note that  as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on &amp;quot;No thanks&amp;quot; in order to finish your Project Week registration.&lt;br /&gt;
*'''Registration Fee:''' $300.&lt;br /&gt;
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.&lt;br /&gt;
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2014_Summer_Project_Week/RoomSharing|here]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== '''Registrants''' ==&lt;br /&gt;
&lt;br /&gt;
Do not add your name to this list - it is maintained by the organizers based on your paid registration.  ([https://www.regonline.com/namic2014summerprojectweek  Please click here to register.])&lt;br /&gt;
&lt;br /&gt;
#Hugo Aerts, Dana Farber/Harvard, hugo_aerts@dfci.harvard.edu&lt;br /&gt;
#Peter Anderson, retired, traneus@verizon.net&lt;br /&gt;
#Nicole Aucoin, Brigham &amp;amp; Women's Hospital, nicole@bwh.harvard.edu&lt;br /&gt;
#Kanglin Chen, Fraunhofer MEVIS, kanglin.chen@mevis.fraunhofer.de&lt;br /&gt;
#Alexander Derksen, Fraunhofer MEVIS, alexander.derksen@mevis.fraunhofer.de&lt;br /&gt;
#Fotis Drakopoulos, Old Dominion University, fdrakopo@gmail.com&lt;br /&gt;
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com&lt;br /&gt;
#Saurabh Jain, Johns Hopkins University, saurabh@cis.jhu.edu&lt;br /&gt;
#Hans Johnson, University of Iowa, hans-johnson@uiowa.edu&lt;br /&gt;
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu&lt;br /&gt;
#Siqi Liu, University of Sydney, sliu4512@uni.sydney.edu.au&lt;br /&gt;
#Bradley Lowekamp, National Institutes of Health, blowekamp@mail.nih.gov&lt;br /&gt;
#Murat Maga, Seattle Children's Research Institute, maga@uw.edu&lt;br /&gt;
#Jim Miller, GE Research, millerjv@ge.com&lt;br /&gt;
#Steve Pieper, Isomics Inc, pieper@isomics.com&lt;br /&gt;
#Csaba Pinter, Queen's University, csaba.pinter@queensu.ca &lt;br /&gt;
#Adam Rankin, Queen's University, rankin@queensu.ca&lt;br /&gt;
#David Welch, University of Iowa, david-welch@uiowa.edu&lt;br /&gt;
#Ryan Young, Seattle Children's Research Institute, ryan.young@seattlechildrens.org&lt;br /&gt;
#Paolo Zaffino, University Magna Graecia of Catanzaro, p.zaffino@unicz.it&lt;br /&gt;
#Fan Zhang, University of Sydney, fzha8048@uni.sydney.edu.au&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=File:Namic_2014_tannenbaum.ppt&amp;diff=84695</id>
		<title>File:Namic 2014 tannenbaum.ppt</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=File:Namic_2014_tannenbaum.ppt&amp;diff=84695"/>
		<updated>2014-01-09T10:42:14Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=AHM_2014_alg_core_presentations&amp;diff=84694</id>
		<title>AHM 2014 alg core presentations</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=AHM_2014_alg_core_presentations&amp;diff=84694"/>
		<updated>2014-01-09T10:39:27Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Alg core presentations */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Alg core presentations ==&lt;br /&gt;
&lt;br /&gt;
*  [[media:Utah1-AHM2014.pptx‎ | Utah 1, Ross Whitaker ]]&lt;br /&gt;
* [[media:NAMIC-AHM-MIT-2014.pptx | MIT, Polina Golland]]&lt;br /&gt;
* Utah 2, Guido Gerig&lt;br /&gt;
* [[media:UNC_AHM2014.pptx‎ | UNC, Martin Styner ]]&lt;br /&gt;
* [[media:Namic_2014_tannenbaum.ppt | Stony Brook, Allen Tannenbaum]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83713</id>
		<title>Algorithm:Main</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83713"/>
		<updated>2013-11-15T23:33:01Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Cores|NA-MIC Cores]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
='''Welcome to the NA-MIC Algorithms!'''=&lt;br /&gt;
{| border=&amp;quot;00&amp;quot; cellpadding=&amp;quot;5&amp;quot; cellspacing=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot;|&lt;br /&gt;
The Algorithms Core (NA-MIC Core 1) investigates mathematical methods for medical image analysis.  Algorithm design is an essential step in attempting to answer clinical questions based on medical imagery.  The Algorithms Core collaborates with Core 2 to provide tools which Core 3 can leverage to enhance clinical studies.  Our team members represent a broad cross-section of medical image groups.  The links below showcase the work at each of the Core 1 sites and also showcase the collaborative work which we share with our Core 2 and 3 partners.&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot; colspan=&amp;quot;2&amp;quot; align=&amp;quot;center&amp;quot;| Volumetric Segmentation of the Arcuate Fasciculus[[Image:UtahArcuateFasciculusFeb10.png|center|200px]]&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;| &lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;|Microstructural Connectivity of the Arcuate Fasciculus in Adolescents with High-functioning Autism [[Algorithm:Past_Featured_Articles|Read more...]] &lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Site =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:BU|Stony Brook]]&lt;br /&gt;
* [[Algorithm:MIT|MIT]]&lt;br /&gt;
* [[Algorithm:UNC|UNC]]&lt;br /&gt;
* [[Algorithm:Utah|Utah 1]]&lt;br /&gt;
* [[Algorithm:Utah2|Utah 2]]&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Subject Matter =&lt;br /&gt;
&lt;br /&gt;
* [[NA-MIC_Collaborations:New|NA-MIC Collaborations]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Data =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:DataRT|Radiotherapy]]&lt;br /&gt;
&lt;br /&gt;
= [[Algorithm:SlicerModules|Slicer Modules]] =&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83712</id>
		<title>Algorithm:Main</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83712"/>
		<updated>2013-11-15T23:32:06Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Cores|NA-MIC Cores]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
='''Welcome to the NA-MIC Algorithms!'''=&lt;br /&gt;
{| border=&amp;quot;00&amp;quot; cellpadding=&amp;quot;5&amp;quot; cellspacing=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot;|&lt;br /&gt;
The Algorithms Core (NA-MIC Core 1) investigates mathematical methods for medical image analysis.  Algorithm design is an essential step in attempting to answer clinical questions based on medical imagery.  The Algorithms Core collaborates with Core 2 to provide tools which Core 3 can leverage to enhance clinical studies.  Our team members represent a broad cross-section of medical image groups.  The links below showcase the work at each of the Core 1 sites and also showcase the collaborative work which we share with our Core 2 and 3 partners.&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot; colspan=&amp;quot;2&amp;quot; align=&amp;quot;center&amp;quot;| Volumetric Segmentation of the Arcuate Fasciculus[[Image:UtahArcuateFasciculusFeb10.png|center|200px]]&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;| &lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;|Microstructural Connectivity of the Arcuate Fasciculus in Adolescents with High-functioning Autism [[Algorithm:Past_Featured_Articles|Read more...]] &lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Site =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:SBU|Stony Brook]]&lt;br /&gt;
* [[Algorithm:MIT|MIT]]&lt;br /&gt;
* [[Algorithm:UNC|UNC]]&lt;br /&gt;
* [[Algorithm:Utah|Utah 1]]&lt;br /&gt;
* [[Algorithm:Utah2|Utah 2]]&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Subject Matter =&lt;br /&gt;
&lt;br /&gt;
* [[NA-MIC_Collaborations:New|NA-MIC Collaborations]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Data =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:DataRT|Radiotherapy]]&lt;br /&gt;
&lt;br /&gt;
= [[Algorithm:SlicerModules|Slicer Modules]] =&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=People&amp;diff=83638</id>
		<title>People</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=People&amp;diff=83638"/>
		<updated>2013-11-03T23:03:17Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Personnel at least partially funded by NA-MIC ==&lt;br /&gt;
&lt;br /&gt;
#'''Leadership Core'''&lt;br /&gt;
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Naucoin|Nicole Aucoin]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)&lt;br /&gt;
## Wendy Plesniak, Harvard (BWH SPL) &lt;br /&gt;
# '''Algorithms Core'''&lt;br /&gt;
## University of Utah C1&lt;br /&gt;
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''&lt;br /&gt;
### Manasi Datar&lt;br /&gt;
### Samuel Gerber&lt;br /&gt;
### Yongshen Pan&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''&lt;br /&gt;
### Emmanuel Bitaud&lt;br /&gt;
### Stanley Dirrleman&lt;br /&gt;
### James Fishbaugh&lt;br /&gt;
### Marcel Prastawa&lt;br /&gt;
### [[User:Gcasey|Casey Goodlett]]&lt;br /&gt;
### Preston T. Fletcher&lt;br /&gt;
### Ran Tao&lt;br /&gt;
## MIT (CSAIL)&lt;br /&gt;
### '''[[Polina_Golland|Polina Golland]], PI'''&lt;br /&gt;
### [[Eric_Grimson|Eric Grimson]]&lt;br /&gt;
###  [[User:FernD|Fern DeOliveira]]&lt;br /&gt;
### Michal Depa&lt;br /&gt;
### Tammy Riklin Raviv&lt;br /&gt;
## UNC&lt;br /&gt;
### '''[[User:Styner|Martin Styner]], PI'''&lt;br /&gt;
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]&lt;br /&gt;
### Michael Garret Larson&lt;br /&gt;
### Gary Long&lt;br /&gt;
### Deepika Mahalingam&lt;br /&gt;
### Ipek Oguz&lt;br /&gt;
### Rachel Gimpel Smith&lt;br /&gt;
### Clement Vachet&lt;br /&gt;
##  Stony Brook University&lt;br /&gt;
### '''[http://iss.bu.edu/tannenba/ Allen Tannenbaum], PI'''&lt;br /&gt;
### Yi Gao&lt;br /&gt;
### Arie Nakhmani&lt;br /&gt;
### Ivan Kolesov&lt;br /&gt;
### Peter Karasev&lt;br /&gt;
## MGH &lt;br /&gt;
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]&lt;br /&gt;
## Kitware, Inc.&lt;br /&gt;
###'''[[User:Will| Will Schroeder]],  PI'''&lt;br /&gt;
### [[User:Barre|Sebastien Barre]]&lt;br /&gt;
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]&lt;br /&gt;
### [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
## GE&lt;br /&gt;
### '''[[User:Millerjv|Jim Miller]], PI'''&lt;br /&gt;
### Roshni Bhagalia&lt;br /&gt;
### Dirk Padfield&lt;br /&gt;
### James Ross&lt;br /&gt;
### [[User:Taox|Xiaodong Tao]]&lt;br /&gt;
### [[User:Harveerar|Harini Veeraraghavan]]&lt;br /&gt;
### [[User:kedar_p|Kedar Patwardhan]]&lt;br /&gt;
## Isomics&lt;br /&gt;
### '''[[User:Pieper|Steve Pieper]], PI'''&lt;br /&gt;
### Alex Yarmakovich&lt;br /&gt;
## WUSTL&lt;br /&gt;
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''&lt;br /&gt;
### Kevin Archie&lt;br /&gt;
### Mikhail Milchenko&lt;br /&gt;
### Timothy Olsen&lt;br /&gt;
## UCSD&lt;br /&gt;
### '''Jeffrey S. Grethe, PI'''&lt;br /&gt;
### Mark Ellisman&lt;br /&gt;
### Marco Ruiz&lt;br /&gt;
# '''Driving Biological Projects (DBP)'''&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''&lt;br /&gt;
### Josh Blauer&lt;br /&gt;
### Josh Cates&lt;br /&gt;
## Iowa&lt;br /&gt;
### '''Hans Johnson, PI'''&lt;br /&gt;
### Norman Williams&lt;br /&gt;
### [[User:Dmwelch | Dave Welch]]&lt;br /&gt;
### Eun Young &amp;quot;Regina&amp;quot; Kim&lt;br /&gt;
### Joy Matsui&lt;br /&gt;
### Ali Ghayhoor&lt;br /&gt;
### Chen Yang&lt;br /&gt;
## MGH&lt;br /&gt;
### '''Gregory C. Sharp, PI'''&lt;br /&gt;
### Rui Li&lt;br /&gt;
## UCLA&lt;br /&gt;
### '''Jack Van Horn, PI'''&lt;br /&gt;
### Jeffrey Alger&lt;br /&gt;
### Ian Bowman&lt;br /&gt;
### Celia Cheung&lt;br /&gt;
### Nathan Hageman&lt;br /&gt;
### David Hovda&lt;br /&gt;
### Arthur W. Toga&lt;br /&gt;
### Paul Vespa&lt;br /&gt;
## Queen's University&lt;br /&gt;
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''&lt;br /&gt;
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]&lt;br /&gt;
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]&lt;br /&gt;
### Siddharth Vikal&lt;br /&gt;
## The Mind Institute&lt;br /&gt;
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''&lt;br /&gt;
### Mark Scully&lt;br /&gt;
## BWH&lt;br /&gt;
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''&lt;br /&gt;
### Jorge Alvarado&lt;br /&gt;
### Jennifer Goodrich&lt;br /&gt;
### Padmapriya Srinivazan&lt;br /&gt;
# '''Service Core'''&lt;br /&gt;
## Kitware, Inc. &lt;br /&gt;
### '''[[User:Will|Will Schroeder]], PI'''&lt;br /&gt;
### Zack Galbreath&lt;br /&gt;
### William Hoffman&lt;br /&gt;
### Julien Jomier&lt;br /&gt;
### Zach Mullen&lt;br /&gt;
# '''Training Core'''&lt;br /&gt;
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Randy|Randy Gollub]], Harvard (MGH) &lt;br /&gt;
# '''Dissemination Core'''&lt;br /&gt;
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''&lt;br /&gt;
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''&lt;br /&gt;
# '''Administration Core'''&lt;br /&gt;
## Rachana Manandhar, Harvard (BWH SPL)&lt;br /&gt;
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)&lt;br /&gt;
&lt;br /&gt;
== NA-MIC Collaborators ==&lt;br /&gt;
These NA-MIC collaborators are funded under the &amp;quot;Collaboration with NCBC&amp;quot; PAR.&lt;br /&gt;
#Nicole Grosland, UIowa&lt;br /&gt;
#Vincent Magnotta, UIowa&lt;br /&gt;
#Steve Pieper, Isomics&lt;br /&gt;
#James Daunais, Wake Forest&lt;br /&gt;
#Robert Kraft, Wake Forest&lt;br /&gt;
#Chris Wyatt, Virginia Tech&lt;br /&gt;
#Kilian Pohl, Harvard (BWH SPL)&lt;br /&gt;
#Sandy Wells, Harvard (BWH SPL)&lt;br /&gt;
#Kevin Cleary, Georgetown&lt;br /&gt;
#Enrique Campos-Nanez, George Washington U.&lt;br /&gt;
#Patrick (Peng) Cheng, Georgetown&lt;br /&gt;
#Ziv Yaniv, Georgetown&lt;br /&gt;
#Nobuhiko Hata, Harvard (BWH)&lt;br /&gt;
#Curtis Lisle, KnowledgeVis&lt;br /&gt;
&lt;br /&gt;
==NA-MIC EAB==&lt;br /&gt;
&lt;br /&gt;
Our External Advisory Board members are listed [[EAB|here]].&lt;br /&gt;
&lt;br /&gt;
== NA-MIC alumni ==&lt;br /&gt;
* [http://marchingcubes.org Bill Lorensen]&lt;br /&gt;
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)&lt;br /&gt;
*  Lauren O'Donnell, MIT (CSAIL)&lt;br /&gt;
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)&lt;br /&gt;
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)&lt;br /&gt;
*  Ramsey Al-Hakim, Georgia Tech&lt;br /&gt;
*  [[User:Melonakos|John Melonakos]], Georgia Tech&lt;br /&gt;
*  [[User:Lankton|Shawn Lankton]], Georgia Tech&lt;br /&gt;
*  [[User:Nain|Delphine Nain]], Georgia Tech&lt;br /&gt;
*   Xavier Le Faucheur, Georgia Tech&lt;br /&gt;
*  [[User:Mohan|Vandana Mohan]], Georgia Tech&lt;br /&gt;
*  Tom Fletcher, Utah&lt;br /&gt;
*  [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah&lt;br /&gt;
*  [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah&lt;br /&gt;
* Josh Snyder, Harvard (MGH)&lt;br /&gt;
* [[User:DavidTuch|David Tuch]], Harvard (MGH)&lt;br /&gt;
* [[User:Karthik|Karthik Krishnan]],Kitware&lt;br /&gt;
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware&lt;br /&gt;
* [[User:Mathieu|Mathieu Malaterre]], Kitware&lt;br /&gt;
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA&lt;br /&gt;
* [[User:MichaelPan|Michael Pan]], UCLA&lt;br /&gt;
* Brendan Flaherty, UCSD&lt;br /&gt;
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)&lt;br /&gt;
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)&lt;br /&gt;
* Martha Shenton, Harvard (BWH PNL) PI&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI&lt;br /&gt;
* Bob Roth, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth&lt;br /&gt;
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth&lt;br /&gt;
* Alan Green, Dartmouth&lt;br /&gt;
* John West, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth&lt;br /&gt;
* Stephen Guerin, Dartmouth&lt;br /&gt;
* John MacDonald, Dartmouth&lt;br /&gt;
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI&lt;br /&gt;
* [[User:Jfallon|James Fallon]], UCI&lt;br /&gt;
* Jessica Turner, UCI&lt;br /&gt;
* Martina Panzenboeck, UCI&lt;br /&gt;
* David Medina, UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI&lt;br /&gt;
* Diane Highum, UCI&lt;br /&gt;
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI&lt;br /&gt;
* Liv Trondsen, UCI&lt;br /&gt;
* Fabio Macciardi, Toronto&lt;br /&gt;
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto&lt;br /&gt;
* Aristotle Voineskos, Toronto&lt;br /&gt;
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST&lt;br /&gt;
&lt;br /&gt;
== &amp;quot;Friends and Family&amp;quot; ==&lt;br /&gt;
&lt;br /&gt;
=== NIH ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Lysterp|Peter M. Lyster]]&lt;br /&gt;
&lt;br /&gt;
=== mBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Akolasny|Anthony Kolasny]]&lt;br /&gt;
* [[User:Dmarcus|Dan Marcus]]&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== fBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== IGT ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
* [[User:Noby| Nobuhiko Hata]]&lt;br /&gt;
&lt;br /&gt;
=== Other ===&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:SlicerModules&amp;diff=83176</id>
		<title>Algorithm:SlicerModules</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:SlicerModules&amp;diff=83176"/>
		<updated>2013-08-31T19:37:58Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;= Algorithms Core: Slicer Modules Under Development = &lt;br /&gt;
These modules will become available as extensions in the Slicer Extension Manager&lt;br /&gt;
&lt;br /&gt;
* Left atrium segmentation: In collaboration with the Afib DBP.  Graph-cut based segmentation that loads meshes, allows users to interactively &amp;quot;center&amp;quot; the model, perform the graph-cut segmentation, scan convert results into a volumetric format.  Contact: Gopal Veni, University of Utah.&lt;br /&gt;
&lt;br /&gt;
* Multimaterial meshing:  Surface and volumetric meshing of multimaterial volumes with output surface triangles viewable within Slicer.  Contact: Jonathan Bronson, University of Utah.&lt;br /&gt;
&lt;br /&gt;
* Interactive segmentation: Control based interactive segmentation module, allowing users to use feedback and observer based principles to drive active contours to equilibrium position and capture desired features. Contact: Ivan Kolesov, SUNY Stony Brook.&lt;br /&gt;
&lt;br /&gt;
* Sobolev active contours: Robust implementation of the active contour methodology using a Sobolev norm, giving much better results in the presence of noise. Contact: Arie Nakhmani, UAB.&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83087</id>
		<title>Algorithm:Main</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83087"/>
		<updated>2013-08-08T00:42:03Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Cores|NA-MIC Cores]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
='''Welcome to the NA-MIC Algorithms!'''=&lt;br /&gt;
{| border=&amp;quot;00&amp;quot; cellpadding=&amp;quot;5&amp;quot; cellspacing=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot;|&lt;br /&gt;
The Algorithms Core (NA-MIC Core 1) investigates mathematical methods for medical image analysis.  Algorithm design is an essential step in attempting to answer clinical questions based on medical imagery.  The Algorithms Core collaborates with Core 2 to provide tools which Core 3 can leverage to enhance clinical studies.  Our team members represent a broad cross-section of medical image groups.  The links below showcase the work at each of the Core 1 sites and also showcase the collaborative work which we share with our Core 2 and 3 partners.&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot; colspan=&amp;quot;2&amp;quot; align=&amp;quot;center&amp;quot;| Volumetric Segmentation of the Arcuate Fasciculus[[Image:UtahArcuateFasciculusFeb10.png|center|200px]]&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;| &lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;|Microstructural Connectivity of the Arcuate Fasciculus in Adolescents with High-functioning Autism [[Algorithm:Past_Featured_Articles|Read more...]] &lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Site =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:BU|Stony Brook]]&lt;br /&gt;
* [[Algorithm:MIT|MIT]]&lt;br /&gt;
* [[Algorithm:UNC|UNC]]&lt;br /&gt;
* [[Algorithm:Utah|Utah 1]]&lt;br /&gt;
* [[Algorithm:Utah2|Utah 2]]&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Subject Matter =&lt;br /&gt;
&lt;br /&gt;
* [[NA-MIC_Collaborations:New|NA-MIC Collaborations]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Data =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:DataRT|Radiotherapy]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=83086</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=83086"/>
		<updated>2013-08-08T00:32:15Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of SUNY Stony Brook Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At the State University of New York at Stony Brook, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Stony Brook University Projects =&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Heart_topology.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
&lt;br /&gt;
Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
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Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
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In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
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We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
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Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
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Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
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The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), submitted.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), to appear.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Table1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83085</id>
		<title>Algorithm:Main</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83085"/>
		<updated>2013-08-08T00:31:17Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Cores|NA-MIC Cores]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
='''Welcome to the NA-MIC Algorithms!'''=&lt;br /&gt;
{| border=&amp;quot;00&amp;quot; cellpadding=&amp;quot;5&amp;quot; cellspacing=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot;|&lt;br /&gt;
The Algorithms Core (NA-MIC Core 1) investigates mathematical methods for medical image analysis.  Algorithm design is an essential step in attempting to answer clinical questions based on medical imagery.  The Algorithms Core collaborates with Core 2 to provide tools which Core 3 can leverage to enhance clinical studies.  Our team members represent a broad cross-section of medical image groups.  The links below showcase the work at each of the Core 1 sites and also showcase the collaborative work which we share with our Core 2 and 3 partners.&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot; colspan=&amp;quot;2&amp;quot; align=&amp;quot;center&amp;quot;| Volumetric Segmentation of the Arcuate Fasciculus[[Image:UtahArcuateFasciculusFeb10.png|center|200px]]&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;| &lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;|Microstructural Connectivity of the Arcuate Fasciculus in Adolescents with High-functioning Autism [[Algorithm:Past_Featured_Articles|Read more...]] &lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Site =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:BU|State University of New York at Stony Brook]]&lt;br /&gt;
* [[Algorithm:MIT|MIT]]&lt;br /&gt;
* [[Algorithm:UNC|UNC]]&lt;br /&gt;
* [[Algorithm:Utah|Utah 1]]&lt;br /&gt;
* [[Algorithm:Utah2|Utah 2]]&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Subject Matter =&lt;br /&gt;
&lt;br /&gt;
* [[NA-MIC_Collaborations:New|NA-MIC Collaborations]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Data =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:DataRT|Radiotherapy]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83084</id>
		<title>Algorithm:Main</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:Main&amp;diff=83084"/>
		<updated>2013-08-08T00:30:23Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Cores|NA-MIC Cores]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
='''Welcome to the NA-MIC Algorithms!'''=&lt;br /&gt;
{| border=&amp;quot;00&amp;quot; cellpadding=&amp;quot;5&amp;quot; cellspacing=&amp;quot;0&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot;|&lt;br /&gt;
The Algorithms Core (NA-MIC Core 1) investigates mathematical methods for medical image analysis.  Algorithm design is an essential step in attempting to answer clinical questions based on medical imagery.  The Algorithms Core collaborates with Core 2 to provide tools which Core 3 can leverage to enhance clinical studies.  Our team members represent a broad cross-section of medical image groups.  The links below showcase the work at each of the Core 1 sites and also showcase the collaborative work which we share with our Core 2 and 3 partners.&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot; colspan=&amp;quot;2&amp;quot; align=&amp;quot;center&amp;quot;| Volumetric Segmentation of the Arcuate Fasciculus[[Image:UtahArcuateFasciculusFeb10.png|center|200px]]&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;| &lt;br /&gt;
| style=&amp;quot;background: #ebeced&amp;quot;|Microstructural Connectivity of the Arcuate Fasciculus in Adolescents with High-functioning Autism [[Algorithm:Past_Featured_Articles|Read more...]] &lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Site =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:Stony Brook|State University of New York at Stony Brook]]&lt;br /&gt;
* [[Algorithm:MIT|MIT]]&lt;br /&gt;
* [[Algorithm:UNC|UNC]]&lt;br /&gt;
* [[Algorithm:Utah|Utah 1]]&lt;br /&gt;
* [[Algorithm:Utah2|Utah 2]]&lt;br /&gt;
&lt;br /&gt;
= Algorithms Projects - Organized by Subject Matter =&lt;br /&gt;
&lt;br /&gt;
* [[NA-MIC_Collaborations:New|NA-MIC Collaborations]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Data =&lt;br /&gt;
&lt;br /&gt;
* [[Algorithm:DataRT|Radiotherapy]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=83083</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=83083"/>
		<updated>2013-08-08T00:29:33Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of SUNY Stony Brook Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At the State University of New York at Stony Brook, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Heart_topology.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
&lt;br /&gt;
Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:HandTracking.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Gao, Y. and Corn, B. and Schifter, D. and Tannenbaum, A. Multiscale 3D Shape Representation and Segmentation with Applications to Hippocampal/Caudate Extraction from Brain MRI, Medical Image Analysis. 16(2) pp374, 2012&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
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Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
&lt;br /&gt;
The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), submitted.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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| | [[Image:ScarSeg_EM.png|200px]]&lt;br /&gt;
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), to appear.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Table1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=2013_Summer_Project_Week&amp;diff=81856</id>
		<title>2013 Summer Project Week</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=2013_Summer_Project_Week&amp;diff=81856"/>
		<updated>2013-06-17T12:37:46Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Events]]&lt;br /&gt;
[[image:PW-MIT2013.png|300px]]&lt;br /&gt;
&lt;br /&gt;
Dates: June 17-21, 2013.&lt;br /&gt;
&lt;br /&gt;
Location: MIT, Cambridge, MA.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Agenda==&lt;br /&gt;
&lt;br /&gt;
{|border=&amp;quot;1&amp;quot;&lt;br /&gt;
|-style=&amp;quot;background:#b0d5e6;color:#02186f&amp;quot; &lt;br /&gt;
!style=&amp;quot;width:10%&amp;quot; |Time&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Monday, June 17&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Tuesday, June 18&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Wednesday, June 19&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Thursday, June 20&lt;br /&gt;
!style=&amp;quot;width:18%&amp;quot; |Friday, June 21&lt;br /&gt;
|-&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#dbdbdb&amp;quot;|'''Project Presentations'''&lt;br /&gt;
|bgcolor=&amp;quot;#6494ec&amp;quot;|'''NA-MIC Update Day'''&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#88aaae&amp;quot;|'''IGT and RT Day'''&lt;br /&gt;
|bgcolor=&amp;quot;#faedb6&amp;quot;|'''Reporting Day'''&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''8:30am'''&lt;br /&gt;
|&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Breakfast&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''9am-12pm'''&lt;br /&gt;
|&lt;br /&gt;
|'''10-11am''' [[2013 Project Week Breakout Session:Slicer4Python|Slicer4 Python Modules, Testing, Q&amp;amp;A]] (Steve Pieper) &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms|Grier Room (Left)]] &lt;br /&gt;
|'''9:30-11pm: &amp;lt;font color=&amp;quot;#4020ff&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt; [[2013 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans Johnson, Brad Lowekamp)&lt;br /&gt;
[[MIT_Project_Week_Rooms#32-D507|32-D507]]&lt;br /&gt;
|'''10am-12pm: &amp;lt;font color=&amp;quot;#4020ff&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt;[[2013 Project Week Breakout Session: IGT|Image-Guided Therapy]] (Tina Kapur)&lt;br /&gt;
[[MIT_Project_Week_Rooms#32-D407|32-D407]]&lt;br /&gt;
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''12pm-1pm'''&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch&lt;br /&gt;
|bgcolor=&amp;quot;#ffffaa&amp;quot;|Lunch boxes; Adjourn by 1:30pm&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''1pm-5:30pm'''&lt;br /&gt;
|'''1-1:05pm: &amp;lt;font color=&amp;quot;#503020&amp;quot;&amp;gt;Ron Kikinis: Welcome&amp;lt;/font&amp;gt;'''&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''3:30-4:30pm''' [[2013 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin)  &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]&lt;br /&gt;
|'''1-3pm:''' [[Renewal-06-2013|NA-MIC Renewal]] &amp;lt;br&amp;gt;PIs &amp;lt;br&amp;gt;Closed Door Session with Ron&lt;br /&gt;
[[MIT_Project_Week_Rooms#32-D407|32-D407]] &lt;br /&gt;
&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;&lt;br /&gt;
'''3-4pm:''' [[2013_Tutorial_Contest|Tutorial Contest Presentations]] &amp;lt;br&amp;gt;&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
|'''12:45-1pm:''' [[Events:TutorialContestJune2013|Tutorial Contest Winner Announcement]]&lt;br /&gt;
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]&lt;br /&gt;
|&amp;lt;br&amp;gt;----------------------------------------&amp;lt;br&amp;gt;'''3-5:30pm: &amp;lt;font color=&amp;quot;#4020ff&amp;quot;&amp;gt;Breakout Session:'''&amp;lt;/font&amp;gt;&amp;lt;br&amp;gt; [[2013 Summer Project Week Breakout Session:RT|Radiation Therapy]] (Greg, Csaba)&lt;br /&gt;
[[MIT_Project_Week_Rooms#32-D407|32-D407]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|bgcolor=&amp;quot;#ffffdd&amp;quot;|'''5:30pm'''&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|bgcolor=&amp;quot;#f0e68b&amp;quot;|Adjourn for the day&lt;br /&gt;
|&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
== '''Projects''' ==&lt;br /&gt;
&lt;br /&gt;
Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template this template] to create wiki pages for your project. Then link the page here with a list of key personnel. &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Huntington's Disease===&lt;br /&gt;
* [[Learn and Apply FiberBundleLabelSelect for Huntington's Disease Data]] (Hans, Demian)&lt;br /&gt;
* [[Investigate Potential Tensor Computation Improvement via Positive Semi-Definite (PSD) Tensor Estimation]] (Hans)&lt;br /&gt;
* [[2013_Summer_Project_Week:SinglePrecisionRegistrationITK| Single Precision Registration]] (Hans, Brad Lowekamp, Dave, Ali Ghayoor)&lt;br /&gt;
* [[Dynamically Configurable Quality Assurance Module for Large Huntington's Disease Database Frontend]] (Dave)&lt;br /&gt;
* [[DWIConvert]] (Dave, Kent Williams)&lt;br /&gt;
* [[Enhance and update SPL atlas]] (Dave, Hans)&lt;br /&gt;
* [[Linear Mixed-effects shape model to explore Huntington's Disease Data]] (Dave, Manasi, Hans, Ross)&lt;br /&gt;
&lt;br /&gt;
===Traumatic Brain Injury===&lt;br /&gt;
* [[Visualization and quantification of peri-contusional white matter bundles in traumatic brain injury using diffusion tensor imaging]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)&lt;br /&gt;
* [[Clinically oriented assessment of local changes in the properties of white matter affected by intra-cranial hemorrhage]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)&lt;br /&gt;
* [[Validation and testing of 3D Slicer modules implementing the Utah segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Guido Gerig, Jack van Horn)&lt;br /&gt;
* [[Exploring multi-modal registration for improved longitudinal modeling of patient-specific 4D DTI data]] (Anuja Sharma, Bo Wang, Andrei Irimia, Micah Chambers, Guido Gerig, Jack van Horn)&lt;br /&gt;
* [[2013_Summer_Project_Week: A Portable Ultrasound Device for Intracranial Hemorrhage Detection|A Portable Ultrasound Device for Intracranial Hemorrhage Detection]] (Jason White, Vicki Noble, Kirby Vosburgh)&lt;br /&gt;
&lt;br /&gt;
===Atrial Fibrillation and Cardiac Image Analysis===&lt;br /&gt;
* [[2013_Summer_Project_Week:Fibrosis_analysis|Fibrosis distribution analysis]] (Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)&lt;br /&gt;
* [[2013_Summer_Project_Week:CARMA_workflow_wizard|Cardiac MRI Toolkit LA segmentation and enhancement quantification workflow wizard]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)&lt;br /&gt;
* [[2013_Summer_Project_Week:CARMA_Documentataion|Cardiac MRI Toolkit Documentation Project]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)&lt;br /&gt;
* [[2013_Summer_Project_Week:CARMA_Visualization|LA model visualization]] (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)&lt;br /&gt;
* [[2013_Summer_Project_Week:CARMA_AutoLASeg|Cardiac MRI Toolkit: Automatic LA Segmentation with Graph Cuts Module]] (Salma Bengali, Alan Morris, Josh Cates, Gopal, Ross Whitaker, Rob MacLeod)&lt;br /&gt;
* [[2013_Summer_Project_Week:Sobolev_Segmenter|Medical Volume Segmentation Using Sobolev Active Contours]] (Arie Nakhmani, Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)&lt;br /&gt;
* [[2013_Summer_Project_Week:Left_Ventricle_Motion_Analysis_using_Tagged_MRI|Left Ventricle Motion Analysis using Tagged MRI]] (Yang Yu, Shaoting Zhang, Dimitris Metaxas)&lt;br /&gt;
&lt;br /&gt;
===Radiation Therapy===&lt;br /&gt;
* [[2013_Summer_Project_Week:Landmark_Registration| Landmark Registration]] (Steve, Nadya, Greg, Paolo, Erol)&lt;br /&gt;
* [[2013_Summer_Project_Week:Slicer_RT:_DICOM-RT_Export | SlicerRT: Dicom-RT Export]] (Greg Sharp, Kevin Wang, Csaba Pinter)&lt;br /&gt;
* [[2013_Summer_Project_Week:Proton_dose_calculation | Proton dose calculation]]  (Greg Sharp, Kevin Wang, Maxime Desplanques)&lt;br /&gt;
* [[2013_Summer_Project_Week:Deformable_registration_validation_toolkit | Deformable registration validation toolkit]] (Greg Sharp, Kevin Wang, Andrey Fedorov, anyone else?)&lt;br /&gt;
* [[2013_Summer_Project_Week:Deformable_transforms | Deformable transform handling in Transforms module]] (Csaba Pinter, Alex Yarmarkovich, Andras Lasso, ?)&lt;br /&gt;
* [[2013_Summer_Project_Week:Brachy_HDR_Ultrasound | Using ultrasound for prostate HDR brachytherapy]] (Adam Rankin)&lt;br /&gt;
* [[Analysis of different atlas-based segmentation techniques for parotid glands]] (Christian Wachinger, Karl Fritscher, Greg Sharp, Matthew Brennan)&lt;br /&gt;
&lt;br /&gt;
===IGT and Device Integration with Slicer===&lt;br /&gt;
* [[2013_Summer_Project_Week:TractAtlasCluster| Tract Atlas and Clustering for Neurosurgery]] (Lauren O'Donnell)&lt;br /&gt;
* [[2013_Summer_Project_Week:SlicerIGT_Extension| SlicerIGT extension]] (Tamas Ungi, Junichi Tokuda, Laurent Chauvin)&lt;br /&gt;
* [[2013_Summer_Project_Week:Ultrasound_Calibration| Ultrasound Calibration]] (Matthew Toews, Daniel Kostro, William Wells, Stephen Aylward, Isaiah Norton, Tamas Ungi)&lt;br /&gt;
* [[2013_Summer_Project_Week:Liver_Trajectory_Management| Liver Trajectory Management]] (Laurent Chauvin, Junichi Tokuda)&lt;br /&gt;
* [[2013_Summer_Project_Week:4DUltrasound| 4D Ultrasound]] (Laurent Chauvin, Junichi Tokuda)&lt;br /&gt;
* [[2013_Summer_Project_Week:LabelMapStatistics| Label map statistics]] (Laurent Chauvin, Csaba Pinter)&lt;br /&gt;
* [[2013_Summer_Project_Week:PerkTutorExtension| Perk Tutor Extension]] (Matthew Holden, Tamas Ungi)&lt;br /&gt;
* [[2013_Summer_Project_Week:Open_source_electromagnetic_trackers_using_OpenIGTLink| Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)&lt;br /&gt;
* [[2013_Summer_Project_Week:3D_prostate_segmentation_of_Ultrasound_image| Segmentation of Prostate Gland from 3D US for Prostate Interventions]] (Xu Li, Andriy Fedorov, Tina Kapur, William Wells)&lt;br /&gt;
* [[2013_Summer_Project_Week:3D_prostate_registration_of_Ultrasound_image_using_MRI| Registration of an MRI delineated image of the Prostate Gland ot a 3D US for Prostate Interventions]] (Demian Wassermann, Andriy Fedorov, Tina Kapur, William Wells)&lt;br /&gt;
&lt;br /&gt;
===Chronic Obstructive Pulmonary Disease===&lt;br /&gt;
* [[2013_Summer_Project_Week:Airway_Inspector_Porting | Porting Airway Inspector to Slicer 4]] (Raul San Jose, Demian Wassermann, Rola Harmouche)&lt;br /&gt;
* [[2013_Summer_Project_Week:MRML_Infrastructure_Airway_Inspector | Airway Inspector: Slicer Extension and MRML Infrastructure]] (Demian Wassermann, Raul San Jose, Rola Harmouche)&lt;br /&gt;
* [[2013_Summer_Project_Week:Nipype_CLI_Integration | Integration of Nipype with CLI modules in the Chest Imaging Platform Library ]] (Rola Harmouche,Demian Wassermann, Raul San Jose)&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== '''Additional Collaborations'''===&lt;br /&gt;
* [[2013_Summer_Project_Week:Radnostics |Spine Segmentation &amp;amp; Osteoporosis Detection In CT Imaging Studies]] (Anthony Blumfield, Ron Kikinis)&lt;br /&gt;
* [[2013_Summer_Project_Week: Computer Assisted Surgery| Computer Assisted Reconstruction of Complex Bone Fractures]] (Karl Fritscher, Peter Karasev, Ivan Kolesov, Allen Tannenbaum, Ron Kikinis)&lt;br /&gt;
* [[2013_Summer_Project_Week:XNAT 3D Viewer| XNAT 3D Viewer]] (Amanda Hartung, Steve Pieper, Daniel Haehn)&lt;br /&gt;
* [[2013_Summer_Project_Week:kukarobot| Interface for the integration of a KUKA robot using OpenIGTLink]] (Sebastian Tauscher, Junichi Tokuda, Nobuhiko Hata)&lt;br /&gt;
* [[2013_Summer_Project_Week:Application of Statistical Shape Modeling to Robot Assisted Spine Surgery | Application of Statistical Shape Modeling to Robot Assisted Spine Surgery]] (Marine Clogenson)&lt;br /&gt;
* [[2013_Summer_Project_Week:Robot_Control| Robot Control]] (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)&lt;br /&gt;
* [[2013_Summer_Project_Week:Epilepsy_Surgery|Identification of MRI Blurring in Temporal Lobe Epilepsy Surgery]] (Luiz Murta)&lt;br /&gt;
* [[2013_Summer_Project_Week:_Is_Neurosurgical_Rigid_Registration_Really_Rigid%3F| Is Neurosurgical Rigid Registration Really Rigid?]] (Athena)&lt;br /&gt;
* [[2013_Summer_Project_Week: Individualized Neuroimaging Content Analysis using 3D Slicer in Alzheimer's Disease| Individualized Neuroimaging Content Analysis using 3D Slicer]] (Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis)&lt;br /&gt;
* [[2013_Summer_Project_Week:CMFReg | Cranio-Maxillofacial Registration]] (Vinicius Boen)&lt;br /&gt;
* [[2013_Summer_Project_Week:DTIPipelineExtensions | DTI Analysis Pipeline as Slicer4 Extensions]] (Vinicius Boen)&lt;br /&gt;
*[[2013_Project_Week:WebbasedAnatomicalTeachingFrameworkSummer2013|Web-based anatomical teaching framework]] (Daniel Haehn, Steve Pieper, Rudolph Pienaar, Lilla Zollei, Nathaniel Reynolds)&lt;br /&gt;
* [[2013_Summer_Project_Week:Biomedical_Image_Computing_Teaching_Modules|3D Slicer based Biomedical image computing teaching modules]]   (A. Vilchis, J-C. Avila-Vilchis, S. Pujol)&lt;br /&gt;
* [[2013_Summer_Project_Week:Analyzing Breast Tumor Heterogeneity Using 3D Slicer|Analyzing Breast Tumor Heterogeneity Using 3D Slicer]] (Sneha Durgapal, Jayender Jagadeesan, Tobias Penzkofer, Vivek Narayan)&lt;br /&gt;
* [[2013_Summer_Project_Week:WMH Segmentation for Stroke|WMH Segmentation for Stroke]] (Adrian Dalca, Ramesh Sridharan, Polina Golland)&lt;br /&gt;
&lt;br /&gt;
==='''Infrastructure'''===&lt;br /&gt;
* [[2013_Summer_Project_Week:MarkupsModuleSummer2013| Markups/Annotations rewrite]] (Nicole Aucoin, Ron Kikinis)&lt;br /&gt;
* [[2013_Summer_Project_Week:Patient_hierarchy | Patient hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper)&lt;br /&gt;
* [[2013_Summer_Project_Week:CLI_Improvements | CLI Improvements (hierarchy nodes, related nodes, roles)]] (Andras Lasso, Csaba Pinter, Jc, Steve, Jim, ?)&lt;br /&gt;
* [[2013_Summer_Project_Week:CLI_Matlab | CLI module implementation in Matlab]] (Andras Lasso, Jc, Steve, Jim, ?)&lt;br /&gt;
* [[2013_Summer_Project_Week:Sample_Data | Sample Data]] (Steve Pieper, Jim Miller, Bill Lorensen, Jc)&lt;br /&gt;
* [[Plastimatch in NiPype]] (Paolo, Dave, Hans)&lt;br /&gt;
* [[2013_Summer_Project_Week:Optimizing start time of slicer| Optimizing start time of slicer]] (Jc, Steve)&lt;br /&gt;
* [[Common resampling and conversion utility functions in Slicer]] (Csaba Pinter, Steve Pieper, Hans, Kevin Wang)&lt;br /&gt;
* [[2013_Summer_Project_Week:CLI_modules_in_MeVisLab| Integrating CTK CLI modules into MeVisLab]] (Hans Meine, Steve, Jc)&lt;br /&gt;
* [[2013_Summer_Project_Week:SimpleITK_GUI| Common Usage GUI for SimpleITK]] (Hans Johnson, Dave Welch, Brad Lowekamp)&lt;br /&gt;
* [[2013_Summer_Project_Week:Extension_dependencies| Extension dependencies]] (Jc, Adam Rankin)&lt;br /&gt;
* [[2013_Summer_Project_Week:Parameter_heirarchy| Parameter Hierarchy]] (Adam Rankin, ??)&lt;br /&gt;
&lt;br /&gt;
== '''Background''' ==&lt;br /&gt;
&lt;br /&gt;
We are pleased to announce the 17th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants.  If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Active preparation begins on Thursday, April 25th at 3pm ET, with a kick-off teleconference.  Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.  &lt;br /&gt;
&lt;br /&gt;
The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work.  The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise.  To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects.  Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.&lt;br /&gt;
&lt;br /&gt;
This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst],  [http://www.cimit.org CIMIT], and OCAIRO.  It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.  &lt;br /&gt;
&lt;br /&gt;
A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== '''Logistics''' ==&lt;br /&gt;
&lt;br /&gt;
*'''Dates:''' June 17-21, 2013.&lt;br /&gt;
*'''Location:''' [[MIT_Project_Week_Rooms| Stata Center / RLE MIT]]. &lt;br /&gt;
*'''REGISTRATION:'''  http://www.regonline.com/namic2013summerprojweek. Please note that  as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on &amp;quot;No thanks&amp;quot; in order to finish your Project Week registration.&lt;br /&gt;
*'''Registration Fee:''' $300.&lt;br /&gt;
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.&lt;br /&gt;
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2013_Summer_Project_Week/RoomSharing|here]]&lt;br /&gt;
&lt;br /&gt;
== '''Preparation''' ==&lt;br /&gt;
&lt;br /&gt;
# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list&lt;br /&gt;
# The NA-MIC engineering team will be discussing projects in a their [http://wiki.na-mic.org/Wiki/index.php/Engineering:TCON_2013 weekly teleconferences]. Participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!&lt;br /&gt;
# By 3pm ET on Thursday May 8, all participants to add a one line title of their project to #Projects&lt;br /&gt;
# By 3pm ET on Thursday June 6, all project leads to complete [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page.  If you have questions, please send an email to tkapur at bwh.harvard.edu.&lt;br /&gt;
# By 3pm on June 13: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)&lt;br /&gt;
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)&lt;br /&gt;
## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)&lt;br /&gt;
## Where possible, setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Matt)&lt;br /&gt;
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...&lt;br /&gt;
# People doing Slicer related projects should come to project week with slicer built on your laptop.&lt;br /&gt;
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.&lt;br /&gt;
## Projects to develop extension modules should be built against the latest Slicer4 trunk.&lt;br /&gt;
&lt;br /&gt;
== '''Registrants''' ==&lt;br /&gt;
&lt;br /&gt;
Do not add your name to this list - it is maintained by the organizers based on your paid registration.  ([http://www.regonline.com/Register/Checkin.aspx?EventID=1233699  Please click here to register.])&lt;br /&gt;
&lt;br /&gt;
#Parth Amin, WIT, aminp@wit.edu&lt;br /&gt;
#Charles Anderson, BWH, canderson26@partners.org&lt;br /&gt;
#Peter Anderson, retired, traneus@verizon.net&lt;br /&gt;
#Nicole Aucoin, BWH, nicole@bwh.harvard.edu&lt;br /&gt;
#Juan Carlos Avila Vilchis, Univ del Estado de Mexico, jc.avila.vilchis@hotmail.com&lt;br /&gt;
#Salma Bengali, Univ UT, salma.bengali@carma.utah.edu&lt;br /&gt;
#Anthony Blumfield, Radnostics, Anthony.Blumfield@Radnostics.com&lt;br /&gt;
#Vinicius Boen, Univ Michigan, vboen@umich.edu&lt;br /&gt;
#Matthew Brennan, MIT, brennanm@mit.edu&lt;br /&gt;
#Francois Budin, NIRAL-UNC, fbudin@unc.edu&lt;br /&gt;
#Ivan Buzurovic, BWH/HMS, ibuzurovic@lroc.harvard.edu&lt;br /&gt;
#Josh Cates, Univ UT, cates@sci.utah.edu&lt;br /&gt;
#Micah Chambers, UCLA, micahcc@ucla.edu&lt;br /&gt;
#Laurent Chauvin, BWH - SPL, lchauvin@bwh.harvard.edu&lt;br /&gt;
#Marine Clogenson, Ecole Polytechnique Federale de Lausanne (Switzerland), marine.clogenson@epfl.ch&lt;br /&gt;
#Adrian Dalca, MIT, adalca@MIT.EDU&lt;br /&gt;
#Matthew D'Artista, BWH - SPL, mdartista7@gmail.com&lt;br /&gt;
#Manasi Datar, Univ UT-SCI Institute, datar@sci.utah.edu&lt;br /&gt;
#Sneha Durgapal, BWH, durgapalsneha@gmail.com&lt;br /&gt;
#Luping Fang, Zhejiang Univ of Technology (China), flp@zjut.edu.cn&lt;br /&gt;
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu&lt;br /&gt;
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com&lt;br /&gt;
#Gregory Fischer, WPI, gfischer@wpi.edu&lt;br /&gt;
#Barton Fiske, zSpace Inc, bfiske@zspace.com&lt;br /&gt;
#Matthew Flynn, WIT, flynnm3@wit.edu&lt;br /&gt;
#Karl Fritscher, MGH, kfritscher@gmail.com&lt;br /&gt;
#Yi Gao, Univ AL Birmingham, gaoyi.cn@gmail.com&lt;br /&gt;
#Maria Gonzalez-Puente, WIT, gonzalezpuentem@wit.edu&lt;br /&gt;
#Daniel Haehn, Boston Childrens Hospital, daniel.haehn@childrens.harvard.edu&lt;br /&gt;
#Michael Halle, BWH-SPL, mhalle@bwh.harvard.edu&lt;br /&gt;
#Rola Harmouche, BWH, rharmo@bwh.harvard.edu&lt;br /&gt;
#Amanda Hartung, Rochester Inst of Tech, amh1646@rit.edu&lt;br /&gt;
#Nobuhiko Hata, BWH, hata@bwh.harvard.edu&lt;br /&gt;
#Nicholas Herlambang, AZE Technology Inc, nicholas.herlambang@azetech.com&lt;br /&gt;
#Matthew Holden, Queen's Univ (Canada), mholden8@cs.queensu.ca&lt;br /&gt;
#Andrei Irimia, UCLA, andrei.irimia@loni.ucla.edu&lt;br /&gt;
#Jayender Jagadeesan, BWH-SPL, jayender@bwh.harvard.edu&lt;br /&gt;
#Hans Johnson, Univ Iowa, hans-johnson@uiowa.edu&lt;br /&gt;
#Tina Kapur, BWH/HMS, tkapur@bwh.harvard.edu&lt;br /&gt;
#Alex Kikinis, BWH, alexkikinis@gmail.com&lt;br /&gt;
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu&lt;br /&gt;
#Nils Klarlund, IEEE, klarlund@ieee.org&lt;br /&gt;
#Daniel Kostro, BWH, dkostro@bwh.harvard.edu&lt;br /&gt;
#Andras Lasso, Queen's Univ (Canada), lasso@cs.queensu.ca&lt;br /&gt;
#Rui Li, GE Global Research, li.rui@ge.com&lt;br /&gt;
#Xu Li, BWH, lixu0103@gmail.com&lt;br /&gt;
#Lichen Liang, MGH, lichenl@nmr.mgh.harvard.edu&lt;br /&gt;
#Sidong Liu, Univ Sydney (Australia), sliu7418@uni.sydney.edu.au&lt;br /&gt;
#William Lorensen, Bill's Basement, bill.lorensen@gmail.com &lt;br /&gt;
#Bradley Lowekamp, Medical Science &amp;amp; Computing Inc, bradley.lowekamp@nih.gov&lt;br /&gt;
#Athena Lyons, Univ Western Australia, 20359511@student.uwa.edu.au&lt;br /&gt;
#Nikos Makris, MGH, nikos@nmr.mgh.harvard.edu&lt;br /&gt;
#Katie Mastrogiacomo, BWH - SPL, kmast@bwh.harvard.edu&lt;br /&gt;
#Alireza Mehrtash, BWH - SPL, mehrtash@bwh.harvard.edu&lt;br /&gt;
#Hans Meine, Fraunhofer MEVIS (Germany), hans.meine@mevis.fraunhofer.de&lt;br /&gt;
#Jim Miller, GE Global Research, millerjv@ge.com&lt;br /&gt;
#Luis Murta, Univ Sao Paulo (Brazil), lomurta@gmail.com&lt;br /&gt;
#Arie Nakhmani, Univ AL Birmingham, anry@uab.edu&lt;br /&gt;
#Isaiah Norton, BWH, inorton@bwh.harvard.edu&lt;br /&gt;
#Lauren O'Donnell, BWH, odonnell@bwh.harvard.edu&lt;br /&gt;
#Dirk Padfield, GE Global Research, padfield@research.ge.com&lt;br /&gt;
#Jian Pan, Zhejiang Univ of Technology (China), pj@zjut.edu.cn&lt;br /&gt;
#George Papadimitriou, MGH, georgep@nmr.mgh.harvard.edu&lt;br /&gt;
#Tobias Penzkofer, BWH - SPL, pt@bwh.harvard.edu&lt;br /&gt;
#Rudolph Pienaar, Boston Childrens Hospital, Rudolph.Pienaar@childrens.harvard.edu&lt;br /&gt;
#Steve Pieper, Isomics Inc, pieper@isomics.com&lt;br /&gt;
#Csaba Pinter, Queen's Univ (Canada), pinter@cs.queensu.ca&lt;br /&gt;
#Sonia Pujol, HMS, spujol@bwh.harvard.edu&lt;br /&gt;
#Adam Rankin, Queen's Univ (Canada), rankin@cs.queensu.ca&lt;br /&gt;
#Nathaniel Reynolds, MGH, reynolds@nmr.mgh.harvard.edu&lt;br /&gt;
#Raul San Jose, BWH, rjosest@bwh.harvard.edu&lt;br /&gt;
#Peter Savadjiev, BWH, petersv@bwh.harvard.edu&lt;br /&gt;
#Anuja Sharma, Univ UT-SCI Institute, anuja@cs.utah.edu&lt;br /&gt;
#Greg Sharp, MGH, gcsharp@partners.org&lt;br /&gt;
#Nadya Shusharina, MGH, nshusharina@partners.org&lt;br /&gt;
#Sebastian Tauscher, Leibniz Univ Hannover (Germany), sebastian.tauscher@imes.uni-hannover.de&lt;br /&gt;
#Clare Tempany, BWH, ctempanyafdhal@partners.org&lt;br /&gt;
#Cyrill von Tiesenhausen, KUKA Laboratories (Germany), cyrill.tiesenhausen@kuka.com&lt;br /&gt;
#Gaurie Tilak, BWH, gaurie_tilak@hms.harvard.edu&lt;br /&gt;
#Matthew Toews, BWH/HMS, mt@bwh.harvard.edu&lt;br /&gt;
#Junichi Tokuda, BWH, tokuda@bwh.harvard.edu&lt;br /&gt;
#Tamas Ungi, Queen's Univ (Canada), ungi@cs.queensu.ca&lt;br /&gt;
#Adriana Vilchis González, Univ del Estado de Mexico, hvigady@hotmail.com&lt;br /&gt;
#Kirby Vosburgh, BWH, kirby@bwh.harvard.edu&lt;br /&gt;
#Christian Wachinger, MIT, wachinge@mit.edu&lt;br /&gt;
#Bo Wang, Univ UT-SCI Institute, bowang@sci.utah.edu&lt;br /&gt;
#Demian Wassermann, BWH, demian@bwh.harvard.edu&lt;br /&gt;
#David Welch, Univ Iowa, david-welch@uiowa.edu&lt;br /&gt;
#William Wells, BWH/HMS, sw@bwh.harvard.edu&lt;br /&gt;
#Phillip White, BWH/HMS, white@bwh.harvard.edu&lt;br /&gt;
#Alex Yarmarkovich, Isomics Inc, alexy@bwh.harvard.edu&lt;br /&gt;
#Kitaro Yoshimitsu, BWH, kitarof1@bwh.harvard.edu&lt;br /&gt;
#Yang Yu, Rutgers Univ, yyu@cs.rutgers.edu&lt;br /&gt;
#Paolo Zaffino, Univ Magna Graecia of Catanzaro (Italy), p.zaffino@unicz.it&lt;br /&gt;
#Lilla Zollei, MGH, lzollei@nmr.mgh.harvard.edu&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=2013_Summer_Project_Week:Sobolev_Segmenter&amp;diff=81193</id>
		<title>2013 Summer Project Week:Sobolev Segmenter</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=2013_Summer_Project_Week:Sobolev_Segmenter&amp;diff=81193"/>
		<updated>2013-05-26T18:22:09Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;__NOTOC__&lt;br /&gt;
&amp;lt;gallery&amp;gt;&lt;br /&gt;
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]&lt;br /&gt;
Image:Sobolev_Segmenter2D.png|The 2D segmentation of tumor by Sobolev active contour.&lt;br /&gt;
Image:Sobolev_Segmenter3D.png|The 3D segmentation of tumor by Sobolev active contour.&lt;br /&gt;
&amp;lt;/gallery&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Key Investigators==&lt;br /&gt;
* UAB: Arie Nakhmani, LiangJia Zhu, Allen Tannenbaum&lt;br /&gt;
* BWH: Yi Gao, Ron Kikinis&lt;br /&gt;
* Utah: Rob MacLeod, Josh Cates&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;margin: 20px;&amp;quot;&amp;gt;&lt;br /&gt;
&amp;lt;div style=&amp;quot;width: 27%; float: left; padding-right: 3%;&amp;quot;&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;h3&amp;gt;Objective&amp;lt;/h3&amp;gt;&lt;br /&gt;
General segmentation tools can be used in a wide range of biomedical applications ranging from tumor delineation to the segmentation of the atrial wall. The latter may be used for the atrial fibrillation DBP.&lt;br /&gt;
The Sobolev active contour is a [http://www.na-mic.org/Wiki/index.php/Projects:SobolevTracker|smooth general 2D segmenter] that can be used in the applications above and is known to be more resistant to noise and local minima those other active contour methodologies. It can be extended for medical volume segmentation. Our objective is to implement Slicer's extension based on Sobolev active contours algorithm for volume segmentation. &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;width: 27%; float: left; padding-right: 3%;&amp;quot;&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;h3&amp;gt;Approach, Plan&amp;lt;/h3&amp;gt;&lt;br /&gt;
We prepare C++ implementation of Sobolev active contour algorithm, and convert it to Slicer Commandline extension.&lt;br /&gt;
This version needs a few sparse initial contours on some slices of the segmented volume.&lt;br /&gt;
&lt;br /&gt;
In another approach, we implement similar algorithm in Python, as an Editor effect of Slicer. In this case, only the definition of 3D depth, and a single click inside the area of the segmented target are needed for the algorithm to work.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;width: 40%; float: left;&amp;quot;&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&amp;lt;h3&amp;gt;Progress&amp;lt;/h3&amp;gt;&lt;br /&gt;
Alpha versions of both approaches (C++ Commandline and Python Editor effect) have been prepared.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
==Delivery Mechanism==&lt;br /&gt;
&lt;br /&gt;
This work will be delivered to the NA-MIC Kit as a loadable Commandline extension and an Editor effect.&lt;br /&gt;
&lt;br /&gt;
==References==&lt;br /&gt;
* A. Nakhmani, A. Tannenbaum, &amp;quot;Tracking with Adaptive Sobolev Snakes.&amp;quot; Submitted to IEEE Transactions on Image Processing. &lt;br /&gt;
&lt;br /&gt;
* A. Nakhmani, A. Tannenbaum, &amp;quot;Self-Crossing Detection and Location for Parametric Active Contours,&amp;quot; IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808, Volume 21, Issue 7, pp. 3150-3156, July 2012.&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Renewal-06-2013&amp;diff=81034</id>
		<title>Renewal-06-2013</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Renewal-06-2013&amp;diff=81034"/>
		<updated>2013-05-14T11:46:11Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Participants */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; [[2013_Summer_Project_Week#Agenda|Back to Summer project week Agenda]]&lt;br /&gt;
&lt;br /&gt;
Closed Door Session with Ron. Room [[MIT_Project_Week_Rooms#32-D407|32-D407]]&lt;br /&gt;
&lt;br /&gt;
=Agenda=&lt;br /&gt;
Getting together in one room will allow us to revisit the overall concept, to fine tune our approach, and to discuss open questions with respect to the grant as a whole.&lt;br /&gt;
=Participants=&lt;br /&gt;
TRD PI's and their delegates.&lt;br /&gt;
*Ron Kikinis&lt;br /&gt;
*Polina Golland (by google hangout)&lt;br /&gt;
*Allen Tannenbaum&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=2013-02-14-Slicer_Demo_at_Memorial_Sloan_Kettering_Cancer_Center&amp;diff=80568</id>
		<title>2013-02-14-Slicer Demo at Memorial Sloan Kettering Cancer Center</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=2013-02-14-Slicer_Demo_at_Memorial_Sloan_Kettering_Cancer_Center&amp;diff=80568"/>
		<updated>2013-03-05T19:25:29Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Attendees */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The goal of this meeting is to demo the segmentation and registration tools in 3D Slicer to the medical physists and radiologists at the Memorial Sloan Kettering Cancer Center (MSKCC).&lt;br /&gt;
&lt;br /&gt;
=Venue=&lt;br /&gt;
MSKCC conference center, Feb 14 @ 11pm&lt;br /&gt;
&lt;br /&gt;
=Attendees=&lt;br /&gt;
* Local Host: Dr. Joseph O. Deasy, Chair, Department of Medical Physics, MSKCC&lt;br /&gt;
* Harini Veeraraghavan, MSKCC&lt;br /&gt;
* Allen Tannenbaum, UAB&lt;br /&gt;
* Yi Gao, BWH&lt;br /&gt;
* Peter Karasev, UAB&lt;br /&gt;
&lt;br /&gt;
=Items for discussion=&lt;br /&gt;
* Level set segmentation tools&lt;br /&gt;
* Interactive segmentation&lt;br /&gt;
* Longitudinal registration&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=2013-02-14-Slicer_Demo_at_Memorial_Sloan_Kettering_Cancer_Center&amp;diff=80567</id>
		<title>2013-02-14-Slicer Demo at Memorial Sloan Kettering Cancer Center</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=2013-02-14-Slicer_Demo_at_Memorial_Sloan_Kettering_Cancer_Center&amp;diff=80567"/>
		<updated>2013-03-05T19:25:07Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Attendees */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The goal of this meeting is to demo the segmentation and registration tools in 3D Slicer to the medical physists and radiologists at the Memorial Sloan Kettering Cancer Center (MSKCC).&lt;br /&gt;
&lt;br /&gt;
=Venue=&lt;br /&gt;
MSKCC conference center, Feb 14 @ 11pm&lt;br /&gt;
&lt;br /&gt;
=Attendees=&lt;br /&gt;
* Local Host: Dr. Joseph O. Deasy, Chair, Department of Medical Physics, MSKCC&lt;br /&gt;
* Harini Veeraraghavan, MSKCC&lt;br /&gt;
* Allen Tannenbaum, UAB&lt;br /&gt;
* Yi Gao, BWH&lt;br /&gt;
* Peter Karasev, GaTech&lt;br /&gt;
&lt;br /&gt;
=Items for discussion=&lt;br /&gt;
* Level set segmentation tools&lt;br /&gt;
* Interactive segmentation&lt;br /&gt;
* Longitudinal registration&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen_v2.pptx&amp;diff=79261</id>
		<title>File:NAMIC-AHM-Jan2013-Allen v2.pptx</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen_v2.pptx&amp;diff=79261"/>
		<updated>2013-01-05T21:24:24Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=79260</id>
		<title>AHM 2013 alg core presentations</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=79260"/>
		<updated>2013-01-05T21:23:48Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; [[AHM_2013#Agenda|Back to AHM_2013 Agenda]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=Presentations=&lt;br /&gt;
&lt;br /&gt;
* Ross Whitaker: [[Media:NAMIC-AHM-Jan2013-Ross.pptx|Slides]]&lt;br /&gt;
* Polina Golland  [[Media:NAMIC-AHM-MIT-2013.pptx|Slides]]&lt;br /&gt;
* Martin Styner&lt;br /&gt;
* Guido Gerig&lt;br /&gt;
* Allen Tannenbaum: [[Media:NAMIC-AHM-Jan2013-Allen_v2.pptx|Slides]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=79259</id>
		<title>AHM 2013 alg core presentations</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=79259"/>
		<updated>2013-01-05T21:22:50Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; [[AHM_2013#Agenda|Back to AHM_2013 Agenda]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=Presentations=&lt;br /&gt;
&lt;br /&gt;
* Ross Whitaker: [[Media:NAMIC-AHM-Jan2013-Ross.pptx|Slides]]&lt;br /&gt;
* Polina Golland  [[Media:NAMIC-AHM-MIT-2013.pptx|Slides]]&lt;br /&gt;
* Martin Styner&lt;br /&gt;
* Guido Gerig&lt;br /&gt;
* Allen Tannenbaum: [[Media:NAMIC-AHM-Jan2013-Allen.pptx|Slides]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=79258</id>
		<title>File:NAMIC-AHM-Jan2013-Allen.pptx</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=79258"/>
		<updated>2013-01-05T21:22:18Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: uploaded a new version of &amp;quot;File:NAMIC-AHM-Jan2013-Allen.pptx&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=79257</id>
		<title>AHM 2013 alg core presentations</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=79257"/>
		<updated>2013-01-05T21:19:46Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; [[AHM_2013#Agenda|Back to AHM_2013 Agenda]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=Presentations=&lt;br /&gt;
&lt;br /&gt;
* Ross Whitaker: [[Media:NAMIC-AHM-Jan2013-Ross.pptx|Slides]]&lt;br /&gt;
* Polina Golland  [[Media:NAMIC-AHM-MIT-2013.pptx|Slides]]&lt;br /&gt;
* Martin Styner&lt;br /&gt;
* Guido Gerig&lt;br /&gt;
* Allen Tannenbaum: [[Media:NAMIC-AHM-Jan2013-Allen_v2.pptx|Slides]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=79256</id>
		<title>File:NAMIC-AHM-Jan2013-Allen.pptx</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=79256"/>
		<updated>2013-01-05T21:18:40Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: uploaded a new version of &amp;quot;File:NAMIC-AHM-Jan2013-Allen.pptx&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=79255</id>
		<title>File:NAMIC-AHM-Jan2013-Allen.pptx</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=79255"/>
		<updated>2013-01-05T21:15:16Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: uploaded a new version of &amp;quot;File:NAMIC-AHM-Jan2013-Allen.pptx&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=78956</id>
		<title>File:NAMIC-AHM-Jan2013-Allen.pptx</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=File:NAMIC-AHM-Jan2013-Allen.pptx&amp;diff=78956"/>
		<updated>2012-12-31T13:33:23Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=78906</id>
		<title>AHM 2013 alg core presentations</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=AHM_2013_alg_core_presentations&amp;diff=78906"/>
		<updated>2012-12-24T21:22:01Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Presentations:&lt;br /&gt;
&lt;br /&gt;
* Ross Whitaker [[Media:NAMIC-AHM-Jan2013-Ross.pptx]]&lt;br /&gt;
* Polina Golland&lt;br /&gt;
* Martin Styner&lt;br /&gt;
* Guido Gerig&lt;br /&gt;
* Allen Tannenbaum [[Media:NAMIC-AHM-Jan2013-Allen.pptx]]&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78333</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78333"/>
		<updated>2012-11-23T22:28:48Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Overview of Boston University Algorithms (PI: Allen Tannenbaum) */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University/UAB Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Heart_topology.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
&lt;br /&gt;
Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
| | [[Image:HandTracking.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
&lt;br /&gt;
The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78329</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78329"/>
		<updated>2012-11-23T20:48:51Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Object Tracking With Adaptive Sobolev Active Contours */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
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| | [[Image:Heart_topology.jpg|200px|]]&lt;br /&gt;
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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
&lt;br /&gt;
Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:HandTracking.png|200px|]]&lt;br /&gt;
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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
In this project we propose adaptive tracking mechanism which can be used in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[File:LASegAxialView.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
&lt;br /&gt;
The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:ScarSeg_EM.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:LongitudinalAFib.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:LRV_Wall.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
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The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
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The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
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The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
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This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
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New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
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We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
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The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
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This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
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|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78328</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78328"/>
		<updated>2012-11-23T20:47:25Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
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&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
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At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
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= Boston University/UAB Projects =&lt;br /&gt;
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| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
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In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
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Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
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Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
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In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
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We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
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Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
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Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
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The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78327</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78327"/>
		<updated>2012-11-23T20:46:01Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Heart_topology.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
&lt;br /&gt;
Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:HandTracking.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
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Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
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The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78326</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78326"/>
		<updated>2012-11-23T20:43:20Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Liver Fibrosis Staging by MRI Analysis */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:HandTracking.png|200px|]]&lt;br /&gt;
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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation.  The proposed Sobolev active contour model overcomes the&lt;br /&gt;
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with&lt;br /&gt;
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and&lt;br /&gt;
low contrast objects. Experimental results show the advantages of our approach compared&lt;br /&gt;
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]&lt;br /&gt;
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| | [[Image:LiverFibrosisHist.png|200px|]]&lt;br /&gt;
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
In this project, we provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current&lt;br /&gt;
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.&lt;br /&gt;
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends&lt;br /&gt;
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has&lt;br /&gt;
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data&lt;br /&gt;
has much lower resolution than histological image data, but it includes the entire liver&lt;br /&gt;
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.&lt;br /&gt;
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)&lt;br /&gt;
in diﬀusion MRI correlates with the ﬁbrosis stage.  [[Projects:LiverFibrosisStaging|More...]]&lt;br /&gt;
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| | [[Image:Heart_topology.jpg|200px|]]&lt;br /&gt;
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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==&lt;br /&gt;
&lt;br /&gt;
Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI&lt;br /&gt;
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation&lt;br /&gt;
locations. In addition, MRI analysis may help to predict if the ablation procedure will help&lt;br /&gt;
a patient or not. Many of these image analysis tasks are largely based on segmentation of&lt;br /&gt;
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses&lt;br /&gt;
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned&lt;br /&gt;
energy function. These moving interfaces are highly aﬀected by image data, which can be&lt;br /&gt;
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of&lt;br /&gt;
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension&lt;br /&gt;
to the principal axis transformation method for ﬁnding robust rigid transformation of two&lt;br /&gt;
volumes. The additional elastic registration is based on a volume registration method&lt;br /&gt;
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[File:LASegAxialView.png|200px]]&lt;br /&gt;
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==&lt;br /&gt;
&lt;br /&gt;
The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical&lt;br /&gt;
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.&lt;br /&gt;
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==&lt;br /&gt;
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can&lt;br /&gt;
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.&lt;br /&gt;
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==&lt;br /&gt;
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]&lt;br /&gt;
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==&lt;br /&gt;
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-&lt;br /&gt;
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.&lt;br /&gt;
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==&lt;br /&gt;
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
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The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
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The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
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The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
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This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
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New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78138</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78138"/>
		<updated>2012-11-19T20:05:28Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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|-&lt;br /&gt;
| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78137</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78137"/>
		<updated>2012-11-19T20:00:44Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78134</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78134"/>
		<updated>2012-11-19T19:52:57Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
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The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
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The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
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The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
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This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
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New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
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We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78111</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78111"/>
		<updated>2012-11-19T04:13:20Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Optimal Mass Transport Registration */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78110</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78110"/>
		<updated>2012-11-19T04:09:49Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Adaptive Radiotherapy for head, neck and thorax */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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&lt;br /&gt;
| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78109</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78109"/>
		<updated>2012-11-19T04:09:02Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Geodesic Tractography Segmentation */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
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A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
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The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
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The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
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This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
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New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
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We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
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The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78108</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78108"/>
		<updated>2012-11-19T04:06:59Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Kernel PCA for Segmentation */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115. &lt;br /&gt;
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| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
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| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78107</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78107"/>
		<updated>2012-11-19T04:02:55Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Re-Orientation Approach for Segmentation of DW-MRI */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78106</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78106"/>
		<updated>2012-11-19T04:02:11Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Re-Orientation Approach for Segmentation of DW-MRI */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
|-&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.  Neuroimage, volume 45, 2009, pp. 123-132.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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| | [[Image:PostRegFleshSkeleton.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
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| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78076</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78076"/>
		<updated>2012-11-19T01:45:04Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Agitation and Pain Assessment Using Digital Imaging */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
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Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
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We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
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The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
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Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
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Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic&lt;br /&gt;
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.  Neuroimage, Mar 2009.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78075</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78075"/>
		<updated>2012-11-19T01:43:23Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Agitation and Pain Assessment Using Digital Imaging */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal drug dosing control for intensive care unit sedation using a hybrid deterministic-stochastic pharmacokinetic and pharmacodynamic&lt;br /&gt;
model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.&lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.  Neuroimage, Mar 2009.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
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| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
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| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78074</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78074"/>
		<updated>2012-11-19T01:39:48Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Particle Filter Registration of Medical Imagery */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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| | [[Image:toT1e1.png|200px|]]&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .&lt;br /&gt;
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.  Neuroimage, Mar 2009.&lt;br /&gt;
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
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| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
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| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
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| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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|-&lt;br /&gt;
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| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
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&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
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| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
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| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
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| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
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| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
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| | [[Image:Table1.png|200px|]]&lt;br /&gt;
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
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| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78073</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78073"/>
		<updated>2012-11-19T01:35:41Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Conformal Flattening (inactive) */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
&lt;br /&gt;
At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
&lt;br /&gt;
= Boston University/UAB Projects =&lt;br /&gt;
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{| cellpadding=&amp;quot;10&amp;quot; style=&amp;quot;text-align:left;&amp;quot;&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
&lt;br /&gt;
Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
&lt;br /&gt;
We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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| | [[Image:GT-SPD-img1.png|200px|]]&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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| | [[Image:3D_Segmentation_LA.png|200px]]&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
&lt;br /&gt;
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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| | [[Image:Pain1.JPG|200px]]&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
&lt;br /&gt;
Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:MultiObjSeg.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
&lt;br /&gt;
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:ShapeBasePstSegSlicer.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
&lt;br /&gt;
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, pp1781, 2010&lt;br /&gt;
.&lt;br /&gt;
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|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
&lt;br /&gt;
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.  Neuroimage, Mar 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
&lt;br /&gt;
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:KVoutSegTightMod.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
&lt;br /&gt;
An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Model3D_upTrans.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
&lt;br /&gt;
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Circle seg.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
| | [[Image:ZoomedResultWithModel.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:P1_small.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
&lt;br /&gt;
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:BasePair3DModel.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:TruckInitialization.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Results brain sag.JPG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech caudateBands.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
&lt;br /&gt;
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Basis membership.png|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Dlpfc1.jpg|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Table1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78072</id>
		<title>Algorithm:BU</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=Algorithm:BU&amp;diff=78072"/>
		<updated>2012-11-19T01:34:20Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Multi-scale Shape Representation and Segmentation With Applications to Radiotherapy */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt; Back to [[Algorithm:Main|NA-MIC Algorithms]]&lt;br /&gt;
__NOTOC__&lt;br /&gt;
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =&lt;br /&gt;
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At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis.  For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome.  The following are many examples of PDE techniques applied to medical image analysis.&lt;br /&gt;
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= Boston University/UAB Projects =&lt;br /&gt;
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==&lt;br /&gt;
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Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]&lt;br /&gt;
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==&lt;br /&gt;
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We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured.  [[Projects:MultiScaleShapeSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.&lt;br /&gt;
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==&lt;br /&gt;
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The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Soft Plaque Detection and Automatic Vessel Segmentation.  PMMIA Workshop in MICCAI, Sep. 2009.&lt;br /&gt;
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==&lt;br /&gt;
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Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.&lt;br /&gt;
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==&lt;br /&gt;
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Pain assessment in patients who are unable to verbally&lt;br /&gt;
communicate with medical staff is a challenging problem&lt;br /&gt;
in patient critical care. The fundamental limitations in sedation&lt;br /&gt;
and pain assessment in the intensive care unit (ICU) stem&lt;br /&gt;
from subjective assessment criteria, rather than quantifiable,&lt;br /&gt;
measurable data for ICU sedation and analgesia. This often&lt;br /&gt;
results in poor quality and inconsistent treatment of patient&lt;br /&gt;
agitation and pain from nurse to nurse. Recent advancements in&lt;br /&gt;
pattern recognition techniques using a relevance vector machine&lt;br /&gt;
algorithm can assist medical staff in assessing sedation and pain&lt;br /&gt;
by constantly monitoring the patient and providing the clinician&lt;br /&gt;
with quantifiable data for ICU sedation. In this paper, we show&lt;br /&gt;
that the pain intensity assessment given by a computer classifier&lt;br /&gt;
has a strong correlation with the pain intensity assessed by&lt;br /&gt;
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship). &lt;br /&gt;
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==&lt;br /&gt;
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Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.&lt;br /&gt;
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==&lt;br /&gt;
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.&lt;br /&gt;
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==&lt;br /&gt;
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, pp1781, 2010&lt;br /&gt;
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==&lt;br /&gt;
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.  Neuroimage, Mar 2009.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==&lt;br /&gt;
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a &amp;quot;soft&amp;quot; tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009.  Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.&lt;br /&gt;
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==&lt;br /&gt;
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)&lt;br /&gt;
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==&lt;br /&gt;
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An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.&lt;br /&gt;
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==&lt;br /&gt;
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We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure.  This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.&lt;br /&gt;
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==&lt;br /&gt;
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99&lt;br /&gt;
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==&lt;br /&gt;
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.&lt;br /&gt;
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==&lt;br /&gt;
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation.  Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty.  We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.  &lt;br /&gt;
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==&lt;br /&gt;
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes&lt;br /&gt;
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.&lt;br /&gt;
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==&lt;br /&gt;
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by&lt;br /&gt;
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.&lt;br /&gt;
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==&lt;br /&gt;
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The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.&lt;br /&gt;
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&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt;  Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.&lt;br /&gt;
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==&lt;br /&gt;
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]&lt;br /&gt;
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==&lt;br /&gt;
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]&lt;br /&gt;
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==&lt;br /&gt;
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]&lt;br /&gt;
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| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Striatum1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Brain-flat.PNG|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:ConformalFlatteningRegistration|Conformal Flattening (inactive)]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig1yan.PNG|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;font color=&amp;quot;red&amp;quot;&amp;gt;'''New: '''&amp;lt;/font&amp;gt; V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Fig67.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Stochastic-snake.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:GT-SulciOutlining1.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==&lt;br /&gt;
&lt;br /&gt;
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Table1.png|200px|]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==&lt;br /&gt;
&lt;br /&gt;
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]&lt;br /&gt;
&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
| | [[Image:Gatech SlicerModel2.jpg|200px]]&lt;br /&gt;
| |&lt;br /&gt;
&lt;br /&gt;
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==&lt;br /&gt;
&lt;br /&gt;
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]&lt;br /&gt;
&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=People&amp;diff=78071</id>
		<title>People</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=People&amp;diff=78071"/>
		<updated>2012-11-19T01:32:42Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Personnel at least partially funded by NA-MIC */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Personnel at least partially funded by NA-MIC ==&lt;br /&gt;
&lt;br /&gt;
#'''Leadership Core'''&lt;br /&gt;
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Naucoin|Nicole Aucoin]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)&lt;br /&gt;
## Wendy Plesniak, Harvard (BWH SPL) &lt;br /&gt;
# '''Algorithms Core'''&lt;br /&gt;
## University of Utah C1&lt;br /&gt;
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''&lt;br /&gt;
### Manasi Datar&lt;br /&gt;
### Samuel Gerber&lt;br /&gt;
### Yongshen Pan&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''&lt;br /&gt;
### Emmanuel Bitaud&lt;br /&gt;
### Stanley Dirrleman&lt;br /&gt;
### James Fishbaugh&lt;br /&gt;
### Marcel Prastawa&lt;br /&gt;
### [[User:Gcasey|Casey Goodlett]]&lt;br /&gt;
### Preston T. Fletcher&lt;br /&gt;
### Ran Tao&lt;br /&gt;
## MIT (CSAIL)&lt;br /&gt;
### '''[[Polina_Golland|Polina Golland]], PI'''&lt;br /&gt;
### [[Eric_Grimson|Eric Grimson]]&lt;br /&gt;
###  [[User:FernD|Fern DeOliveira]]&lt;br /&gt;
### Michal Depa&lt;br /&gt;
### Tammy Riklin Raviv&lt;br /&gt;
## UNC&lt;br /&gt;
### '''[[User:Styner|Martin Styner]], PI'''&lt;br /&gt;
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]&lt;br /&gt;
### Michael Garret Larson&lt;br /&gt;
### Gary Long&lt;br /&gt;
### Deepika Mahalingam&lt;br /&gt;
### Ipek Oguz&lt;br /&gt;
### Rachel Gimpel Smith&lt;br /&gt;
### Clement Vachet&lt;br /&gt;
## Boston University/UAB&lt;br /&gt;
### '''[http://iss.bu.edu/tannenba/ Allen Tannenbaum], PI'''&lt;br /&gt;
### Yi Gao&lt;br /&gt;
### Arie Nakhmani&lt;br /&gt;
### Ivan Kolesov&lt;br /&gt;
### Peter Karasev&lt;br /&gt;
## MGH &lt;br /&gt;
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]&lt;br /&gt;
## Kitware, Inc.&lt;br /&gt;
###'''[[User:Will| Will Schroeder]],  PI'''&lt;br /&gt;
### [[User:Barre|Sebastien Barre]]&lt;br /&gt;
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]&lt;br /&gt;
### [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
## GE&lt;br /&gt;
### '''[[User:Millerjv|Jim Miller]], PI'''&lt;br /&gt;
### Roshni Bhagalia&lt;br /&gt;
### Dirk Padfield&lt;br /&gt;
### James Ross&lt;br /&gt;
### [[User:Taox|Xiaodong Tao]]&lt;br /&gt;
### [[User:Harveerar|Harini Veeraraghavan]]&lt;br /&gt;
### [[User:kedar_p|Kedar Patwardhan]]&lt;br /&gt;
## Isomics&lt;br /&gt;
### '''[[User:Pieper|Steve Pieper]], PI'''&lt;br /&gt;
### Alex Yarmakovich&lt;br /&gt;
## WUSTL&lt;br /&gt;
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''&lt;br /&gt;
### Kevin Archie&lt;br /&gt;
### Mikhail Milchenko&lt;br /&gt;
### Timothy Olsen&lt;br /&gt;
## UCSD&lt;br /&gt;
### '''Jeffrey S. Grethe, PI'''&lt;br /&gt;
### Mark Ellisman&lt;br /&gt;
### Marco Ruiz&lt;br /&gt;
# '''Driving Biological Projects (DBP)'''&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''&lt;br /&gt;
### Josh Blauer&lt;br /&gt;
### Josh Cates&lt;br /&gt;
## Iowa&lt;br /&gt;
### '''Hans Johnson, PI'''&lt;br /&gt;
### Norman Williams&lt;br /&gt;
### [[User:Dmwelch | Dave Welch]]&lt;br /&gt;
### Eun Young &amp;quot;Regina&amp;quot; Kim&lt;br /&gt;
### Joy Matsui&lt;br /&gt;
### Ali Ghayhoor&lt;br /&gt;
### Chen Yang&lt;br /&gt;
## MGH&lt;br /&gt;
### '''Gregory C. Sharp, PI'''&lt;br /&gt;
### Rui Li&lt;br /&gt;
## UCLA&lt;br /&gt;
### '''Jack Van Horn, PI'''&lt;br /&gt;
### Jeffrey Alger&lt;br /&gt;
### Ian Bowman&lt;br /&gt;
### Celia Cheung&lt;br /&gt;
### Nathan Hageman&lt;br /&gt;
### David Hovda&lt;br /&gt;
### Arthur W. Toga&lt;br /&gt;
### Paul Vespa&lt;br /&gt;
## Queen's University&lt;br /&gt;
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''&lt;br /&gt;
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]&lt;br /&gt;
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]&lt;br /&gt;
### Siddharth Vikal&lt;br /&gt;
## The Mind Institute&lt;br /&gt;
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''&lt;br /&gt;
### Mark Scully&lt;br /&gt;
## BWH&lt;br /&gt;
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''&lt;br /&gt;
### Jorge Alvarado&lt;br /&gt;
### Jennifer Goodrich&lt;br /&gt;
### Padmapriya Srinivazan&lt;br /&gt;
# '''Service Core'''&lt;br /&gt;
## Kitware, Inc. &lt;br /&gt;
### '''[[User:Will|Will Schroeder]], PI'''&lt;br /&gt;
### Zack Galbreath&lt;br /&gt;
### William Hoffman&lt;br /&gt;
### Julien Jomier&lt;br /&gt;
### Zach Mullen&lt;br /&gt;
# '''Training Core'''&lt;br /&gt;
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Randy|Randy Gollub]], Harvard (MGH) PI&lt;br /&gt;
# '''Dissemination Core'''&lt;br /&gt;
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''&lt;br /&gt;
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''&lt;br /&gt;
# '''Administration Core'''&lt;br /&gt;
## Rachana Manandhar, Harvard (BWH SPL)&lt;br /&gt;
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)&lt;br /&gt;
&lt;br /&gt;
== NA-MIC Collaborators ==&lt;br /&gt;
These NA-MIC collaborators are funded under the &amp;quot;Collaboration with NCBC&amp;quot; PAR.&lt;br /&gt;
#Nicole Grosland, UIowa&lt;br /&gt;
#Vincent Magnotta, UIowa&lt;br /&gt;
#Steve Pieper, Isomics&lt;br /&gt;
#James Daunais, Wake Forest&lt;br /&gt;
#Robert Kraft, Wake Forest&lt;br /&gt;
#Chris Wyatt, Virginia Tech&lt;br /&gt;
#Kilian Pohl, Harvard (BWH SPL)&lt;br /&gt;
#Sandy Wells, Harvard (BWH SPL)&lt;br /&gt;
#Kevin Cleary, Georgetown&lt;br /&gt;
#Enrique Campos-Nanez, George Washington U.&lt;br /&gt;
#Patrick (Peng) Cheng, Georgetown&lt;br /&gt;
#Ziv Yaniv, Georgetown&lt;br /&gt;
#Nobuhiko Hata, Harvard (BWH)&lt;br /&gt;
#Curtis Lisle, KnowledgeVis&lt;br /&gt;
&lt;br /&gt;
==NA-MIC EAB==&lt;br /&gt;
&lt;br /&gt;
Our External Advisory Board members are listed [[EAB|here]].&lt;br /&gt;
&lt;br /&gt;
== NA-MIC alumni ==&lt;br /&gt;
* [http://marchingcubes.org Bill Lorensen]&lt;br /&gt;
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)&lt;br /&gt;
*  Lauren O'Donnell, MIT (CSAIL)&lt;br /&gt;
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)&lt;br /&gt;
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)&lt;br /&gt;
*  Ramsey Al-Hakim, Georgia Tech&lt;br /&gt;
*  [[User:Melonakos|John Melonakos]], Georgia Tech&lt;br /&gt;
*  [[User:Lankton|Shawn Lankton]], Georgia Tech&lt;br /&gt;
*  [[User:Nain|Delphine Nain]], Georgia Tech&lt;br /&gt;
*   Xavier Le Faucheur, Georgia Tech&lt;br /&gt;
*  [[User:Mohan|Vandana Mohan]], Georgia Tech&lt;br /&gt;
*  Tom Fletcher, Utah&lt;br /&gt;
*  [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah&lt;br /&gt;
*  [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah&lt;br /&gt;
* Josh Snyder, Harvard (MGH)&lt;br /&gt;
* [[User:DavidTuch|David Tuch]], Harvard (MGH)&lt;br /&gt;
* [[User:Karthik|Karthik Krishnan]],Kitware&lt;br /&gt;
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware&lt;br /&gt;
* [[User:Mathieu|Mathieu Malaterre]], Kitware&lt;br /&gt;
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA&lt;br /&gt;
* [[User:MichaelPan|Michael Pan]], UCLA&lt;br /&gt;
* Brendan Flaherty, UCSD&lt;br /&gt;
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)&lt;br /&gt;
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)&lt;br /&gt;
* Martha Shenton, Harvard (BWH PNL) PI&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI&lt;br /&gt;
* Bob Roth, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth&lt;br /&gt;
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth&lt;br /&gt;
* Alan Green, Dartmouth&lt;br /&gt;
* John West, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth&lt;br /&gt;
* Stephen Guerin, Dartmouth&lt;br /&gt;
* John MacDonald, Dartmouth&lt;br /&gt;
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI&lt;br /&gt;
* [[User:Jfallon|James Fallon]], UCI&lt;br /&gt;
* Jessica Turner, UCI&lt;br /&gt;
* Martina Panzenboeck, UCI&lt;br /&gt;
* David Medina, UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI&lt;br /&gt;
* Diane Highum, UCI&lt;br /&gt;
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI&lt;br /&gt;
* Liv Trondsen, UCI&lt;br /&gt;
* Fabio Macciardi, Toronto&lt;br /&gt;
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto&lt;br /&gt;
* Aristotle Voineskos, Toronto&lt;br /&gt;
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST&lt;br /&gt;
&lt;br /&gt;
== &amp;quot;Friends and Family&amp;quot; ==&lt;br /&gt;
&lt;br /&gt;
=== NIH ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Lysterp|Peter M. Lyster]]&lt;br /&gt;
&lt;br /&gt;
=== mBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Akolasny|Anthony Kolasny]]&lt;br /&gt;
* [[User:Dmarcus|Dan Marcus]]&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== fBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== IGT ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
* [[User:Noby| Nobuhiko Hata]]&lt;br /&gt;
&lt;br /&gt;
=== Other ===&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=People&amp;diff=78070</id>
		<title>People</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=People&amp;diff=78070"/>
		<updated>2012-11-19T01:31:43Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Personnel at least partially funded by NA-MIC */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Personnel at least partially funded by NA-MIC ==&lt;br /&gt;
&lt;br /&gt;
#'''Leadership Core'''&lt;br /&gt;
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Naucoin|Nicole Aucoin]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)&lt;br /&gt;
## Wendy Plesniak, Harvard (BWH SPL) &lt;br /&gt;
# '''Algorithms Core'''&lt;br /&gt;
## University of Utah C1&lt;br /&gt;
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''&lt;br /&gt;
### Manasi Datar&lt;br /&gt;
### Samuel Gerber&lt;br /&gt;
### Yongshen Pan&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''&lt;br /&gt;
### Emmanuel Bitaud&lt;br /&gt;
### Stanley Dirrleman&lt;br /&gt;
### James Fishbaugh&lt;br /&gt;
### Marcel Prastawa&lt;br /&gt;
### [[User:Gcasey|Casey Goodlett]]&lt;br /&gt;
### Preston T. Fletcher&lt;br /&gt;
### Ran Tao&lt;br /&gt;
## MIT (CSAIL)&lt;br /&gt;
### '''[[Polina_Golland|Polina Golland]], PI'''&lt;br /&gt;
### [[Eric_Grimson|Eric Grimson]]&lt;br /&gt;
###  [[User:FernD|Fern DeOliveira]]&lt;br /&gt;
### Michal Depa&lt;br /&gt;
### Tammy Riklin Raviv&lt;br /&gt;
## UNC&lt;br /&gt;
### '''[[User:Styner|Martin Styner]], PI'''&lt;br /&gt;
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]&lt;br /&gt;
### Michael Garret Larson&lt;br /&gt;
### Gary Long&lt;br /&gt;
### Deepika Mahalingam&lt;br /&gt;
### Ipek Oguz&lt;br /&gt;
### Rachel Gimpel Smith&lt;br /&gt;
### Clement Vachet&lt;br /&gt;
## Boston University/UAB&lt;br /&gt;
### '''[http://iss.bu.edu/tannenba/Allen Tannenbaum], PI'''&lt;br /&gt;
### Yi Gao&lt;br /&gt;
### Arie Nakhmani&lt;br /&gt;
### Ivan Kolesov&lt;br /&gt;
### Peter Karasev&lt;br /&gt;
## MGH &lt;br /&gt;
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]&lt;br /&gt;
## Kitware, Inc.&lt;br /&gt;
###'''[[User:Will| Will Schroeder]],  PI'''&lt;br /&gt;
### [[User:Barre|Sebastien Barre]]&lt;br /&gt;
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]&lt;br /&gt;
### [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
## GE&lt;br /&gt;
### '''[[User:Millerjv|Jim Miller]], PI'''&lt;br /&gt;
### Roshni Bhagalia&lt;br /&gt;
### Dirk Padfield&lt;br /&gt;
### James Ross&lt;br /&gt;
### [[User:Taox|Xiaodong Tao]]&lt;br /&gt;
### [[User:Harveerar|Harini Veeraraghavan]]&lt;br /&gt;
### [[User:kedar_p|Kedar Patwardhan]]&lt;br /&gt;
## Isomics&lt;br /&gt;
### '''[[User:Pieper|Steve Pieper]], PI'''&lt;br /&gt;
### Alex Yarmakovich&lt;br /&gt;
## WUSTL&lt;br /&gt;
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''&lt;br /&gt;
### Kevin Archie&lt;br /&gt;
### Mikhail Milchenko&lt;br /&gt;
### Timothy Olsen&lt;br /&gt;
## UCSD&lt;br /&gt;
### '''Jeffrey S. Grethe, PI'''&lt;br /&gt;
### Mark Ellisman&lt;br /&gt;
### Marco Ruiz&lt;br /&gt;
# '''Driving Biological Projects (DBP)'''&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''&lt;br /&gt;
### Josh Blauer&lt;br /&gt;
### Josh Cates&lt;br /&gt;
## Iowa&lt;br /&gt;
### '''Hans Johnson, PI'''&lt;br /&gt;
### Norman Williams&lt;br /&gt;
### [[User:Dmwelch | Dave Welch]]&lt;br /&gt;
### Eun Young &amp;quot;Regina&amp;quot; Kim&lt;br /&gt;
### Joy Matsui&lt;br /&gt;
### Ali Ghayhoor&lt;br /&gt;
### Chen Yang&lt;br /&gt;
## MGH&lt;br /&gt;
### '''Gregory C. Sharp, PI'''&lt;br /&gt;
### Rui Li&lt;br /&gt;
## UCLA&lt;br /&gt;
### '''Jack Van Horn, PI'''&lt;br /&gt;
### Jeffrey Alger&lt;br /&gt;
### Ian Bowman&lt;br /&gt;
### Celia Cheung&lt;br /&gt;
### Nathan Hageman&lt;br /&gt;
### David Hovda&lt;br /&gt;
### Arthur W. Toga&lt;br /&gt;
### Paul Vespa&lt;br /&gt;
## Queen's University&lt;br /&gt;
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''&lt;br /&gt;
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]&lt;br /&gt;
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]&lt;br /&gt;
### Siddharth Vikal&lt;br /&gt;
## The Mind Institute&lt;br /&gt;
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''&lt;br /&gt;
### Mark Scully&lt;br /&gt;
## BWH&lt;br /&gt;
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''&lt;br /&gt;
### Jorge Alvarado&lt;br /&gt;
### Jennifer Goodrich&lt;br /&gt;
### Padmapriya Srinivazan&lt;br /&gt;
# '''Service Core'''&lt;br /&gt;
## Kitware, Inc. &lt;br /&gt;
### '''[[User:Will|Will Schroeder]], PI'''&lt;br /&gt;
### Zack Galbreath&lt;br /&gt;
### William Hoffman&lt;br /&gt;
### Julien Jomier&lt;br /&gt;
### Zach Mullen&lt;br /&gt;
# '''Training Core'''&lt;br /&gt;
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Randy|Randy Gollub]], Harvard (MGH) PI&lt;br /&gt;
# '''Dissemination Core'''&lt;br /&gt;
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''&lt;br /&gt;
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''&lt;br /&gt;
# '''Administration Core'''&lt;br /&gt;
## Rachana Manandhar, Harvard (BWH SPL)&lt;br /&gt;
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)&lt;br /&gt;
&lt;br /&gt;
== NA-MIC Collaborators ==&lt;br /&gt;
These NA-MIC collaborators are funded under the &amp;quot;Collaboration with NCBC&amp;quot; PAR.&lt;br /&gt;
#Nicole Grosland, UIowa&lt;br /&gt;
#Vincent Magnotta, UIowa&lt;br /&gt;
#Steve Pieper, Isomics&lt;br /&gt;
#James Daunais, Wake Forest&lt;br /&gt;
#Robert Kraft, Wake Forest&lt;br /&gt;
#Chris Wyatt, Virginia Tech&lt;br /&gt;
#Kilian Pohl, Harvard (BWH SPL)&lt;br /&gt;
#Sandy Wells, Harvard (BWH SPL)&lt;br /&gt;
#Kevin Cleary, Georgetown&lt;br /&gt;
#Enrique Campos-Nanez, George Washington U.&lt;br /&gt;
#Patrick (Peng) Cheng, Georgetown&lt;br /&gt;
#Ziv Yaniv, Georgetown&lt;br /&gt;
#Nobuhiko Hata, Harvard (BWH)&lt;br /&gt;
#Curtis Lisle, KnowledgeVis&lt;br /&gt;
&lt;br /&gt;
==NA-MIC EAB==&lt;br /&gt;
&lt;br /&gt;
Our External Advisory Board members are listed [[EAB|here]].&lt;br /&gt;
&lt;br /&gt;
== NA-MIC alumni ==&lt;br /&gt;
* [http://marchingcubes.org Bill Lorensen]&lt;br /&gt;
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)&lt;br /&gt;
*  Lauren O'Donnell, MIT (CSAIL)&lt;br /&gt;
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)&lt;br /&gt;
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)&lt;br /&gt;
*  Ramsey Al-Hakim, Georgia Tech&lt;br /&gt;
*  [[User:Melonakos|John Melonakos]], Georgia Tech&lt;br /&gt;
*  [[User:Lankton|Shawn Lankton]], Georgia Tech&lt;br /&gt;
*  [[User:Nain|Delphine Nain]], Georgia Tech&lt;br /&gt;
*   Xavier Le Faucheur, Georgia Tech&lt;br /&gt;
*  [[User:Mohan|Vandana Mohan]], Georgia Tech&lt;br /&gt;
*  Tom Fletcher, Utah&lt;br /&gt;
*  [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah&lt;br /&gt;
*  [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah&lt;br /&gt;
* Josh Snyder, Harvard (MGH)&lt;br /&gt;
* [[User:DavidTuch|David Tuch]], Harvard (MGH)&lt;br /&gt;
* [[User:Karthik|Karthik Krishnan]],Kitware&lt;br /&gt;
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware&lt;br /&gt;
* [[User:Mathieu|Mathieu Malaterre]], Kitware&lt;br /&gt;
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA&lt;br /&gt;
* [[User:MichaelPan|Michael Pan]], UCLA&lt;br /&gt;
* Brendan Flaherty, UCSD&lt;br /&gt;
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)&lt;br /&gt;
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)&lt;br /&gt;
* Martha Shenton, Harvard (BWH PNL) PI&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI&lt;br /&gt;
* Bob Roth, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth&lt;br /&gt;
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth&lt;br /&gt;
* Alan Green, Dartmouth&lt;br /&gt;
* John West, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth&lt;br /&gt;
* Stephen Guerin, Dartmouth&lt;br /&gt;
* John MacDonald, Dartmouth&lt;br /&gt;
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI&lt;br /&gt;
* [[User:Jfallon|James Fallon]], UCI&lt;br /&gt;
* Jessica Turner, UCI&lt;br /&gt;
* Martina Panzenboeck, UCI&lt;br /&gt;
* David Medina, UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI&lt;br /&gt;
* Diane Highum, UCI&lt;br /&gt;
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI&lt;br /&gt;
* Liv Trondsen, UCI&lt;br /&gt;
* Fabio Macciardi, Toronto&lt;br /&gt;
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto&lt;br /&gt;
* Aristotle Voineskos, Toronto&lt;br /&gt;
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST&lt;br /&gt;
&lt;br /&gt;
== &amp;quot;Friends and Family&amp;quot; ==&lt;br /&gt;
&lt;br /&gt;
=== NIH ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Lysterp|Peter M. Lyster]]&lt;br /&gt;
&lt;br /&gt;
=== mBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Akolasny|Anthony Kolasny]]&lt;br /&gt;
* [[User:Dmarcus|Dan Marcus]]&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== fBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== IGT ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
* [[User:Noby| Nobuhiko Hata]]&lt;br /&gt;
&lt;br /&gt;
=== Other ===&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=People&amp;diff=78069</id>
		<title>People</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=People&amp;diff=78069"/>
		<updated>2012-11-19T01:30:59Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Personnel at least partially funded by NA-MIC */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Personnel at least partially funded by NA-MIC ==&lt;br /&gt;
&lt;br /&gt;
#'''Leadership Core'''&lt;br /&gt;
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Naucoin|Nicole Aucoin]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)&lt;br /&gt;
## Wendy Plesniak, Harvard (BWH SPL) &lt;br /&gt;
# '''Algorithms Core'''&lt;br /&gt;
## University of Utah C1&lt;br /&gt;
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''&lt;br /&gt;
### Manasi Datar&lt;br /&gt;
### Samuel Gerber&lt;br /&gt;
### Yongshen Pan&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''&lt;br /&gt;
### Emmanuel Bitaud&lt;br /&gt;
### Stanley Dirrleman&lt;br /&gt;
### James Fishbaugh&lt;br /&gt;
### Marcel Prastawa&lt;br /&gt;
### [[User:Gcasey|Casey Goodlett]]&lt;br /&gt;
### Preston T. Fletcher&lt;br /&gt;
### Ran Tao&lt;br /&gt;
## MIT (CSAIL)&lt;br /&gt;
### '''[[Polina_Golland|Polina Golland]], PI'''&lt;br /&gt;
### [[Eric_Grimson|Eric Grimson]]&lt;br /&gt;
###  [[User:FernD|Fern DeOliveira]]&lt;br /&gt;
### Michal Depa&lt;br /&gt;
### Tammy Riklin Raviv&lt;br /&gt;
## UNC&lt;br /&gt;
### '''[[User:Styner|Martin Styner]], PI'''&lt;br /&gt;
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]&lt;br /&gt;
### Michael Garret Larson&lt;br /&gt;
### Gary Long&lt;br /&gt;
### Deepika Mahalingam&lt;br /&gt;
### Ipek Oguz&lt;br /&gt;
### Rachel Gimpel Smith&lt;br /&gt;
### Clement Vachet&lt;br /&gt;
## Boston University/UAB&lt;br /&gt;
### '''[http:iss.bu.edu/tannenba/Allen Tannenbaum], PI'''&lt;br /&gt;
### Yi Gao&lt;br /&gt;
### Arie Nakhmani&lt;br /&gt;
### Ivan Kolesov&lt;br /&gt;
### Peter Karasev&lt;br /&gt;
## MGH &lt;br /&gt;
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]&lt;br /&gt;
## Kitware, Inc.&lt;br /&gt;
###'''[[User:Will| Will Schroeder]],  PI'''&lt;br /&gt;
### [[User:Barre|Sebastien Barre]]&lt;br /&gt;
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]&lt;br /&gt;
### [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
## GE&lt;br /&gt;
### '''[[User:Millerjv|Jim Miller]], PI'''&lt;br /&gt;
### Roshni Bhagalia&lt;br /&gt;
### Dirk Padfield&lt;br /&gt;
### James Ross&lt;br /&gt;
### [[User:Taox|Xiaodong Tao]]&lt;br /&gt;
### [[User:Harveerar|Harini Veeraraghavan]]&lt;br /&gt;
### [[User:kedar_p|Kedar Patwardhan]]&lt;br /&gt;
## Isomics&lt;br /&gt;
### '''[[User:Pieper|Steve Pieper]], PI'''&lt;br /&gt;
### Alex Yarmakovich&lt;br /&gt;
## WUSTL&lt;br /&gt;
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''&lt;br /&gt;
### Kevin Archie&lt;br /&gt;
### Mikhail Milchenko&lt;br /&gt;
### Timothy Olsen&lt;br /&gt;
## UCSD&lt;br /&gt;
### '''Jeffrey S. Grethe, PI'''&lt;br /&gt;
### Mark Ellisman&lt;br /&gt;
### Marco Ruiz&lt;br /&gt;
# '''Driving Biological Projects (DBP)'''&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''&lt;br /&gt;
### Josh Blauer&lt;br /&gt;
### Josh Cates&lt;br /&gt;
## Iowa&lt;br /&gt;
### '''Hans Johnson, PI'''&lt;br /&gt;
### Norman Williams&lt;br /&gt;
### [[User:Dmwelch | Dave Welch]]&lt;br /&gt;
### Eun Young &amp;quot;Regina&amp;quot; Kim&lt;br /&gt;
### Joy Matsui&lt;br /&gt;
### Ali Ghayhoor&lt;br /&gt;
### Chen Yang&lt;br /&gt;
## MGH&lt;br /&gt;
### '''Gregory C. Sharp, PI'''&lt;br /&gt;
### Rui Li&lt;br /&gt;
## UCLA&lt;br /&gt;
### '''Jack Van Horn, PI'''&lt;br /&gt;
### Jeffrey Alger&lt;br /&gt;
### Ian Bowman&lt;br /&gt;
### Celia Cheung&lt;br /&gt;
### Nathan Hageman&lt;br /&gt;
### David Hovda&lt;br /&gt;
### Arthur W. Toga&lt;br /&gt;
### Paul Vespa&lt;br /&gt;
## Queen's University&lt;br /&gt;
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''&lt;br /&gt;
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]&lt;br /&gt;
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]&lt;br /&gt;
### Siddharth Vikal&lt;br /&gt;
## The Mind Institute&lt;br /&gt;
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''&lt;br /&gt;
### Mark Scully&lt;br /&gt;
## BWH&lt;br /&gt;
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''&lt;br /&gt;
### Jorge Alvarado&lt;br /&gt;
### Jennifer Goodrich&lt;br /&gt;
### Padmapriya Srinivazan&lt;br /&gt;
# '''Service Core'''&lt;br /&gt;
## Kitware, Inc. &lt;br /&gt;
### '''[[User:Will|Will Schroeder]], PI'''&lt;br /&gt;
### Zack Galbreath&lt;br /&gt;
### William Hoffman&lt;br /&gt;
### Julien Jomier&lt;br /&gt;
### Zach Mullen&lt;br /&gt;
# '''Training Core'''&lt;br /&gt;
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Randy|Randy Gollub]], Harvard (MGH) PI&lt;br /&gt;
# '''Dissemination Core'''&lt;br /&gt;
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''&lt;br /&gt;
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''&lt;br /&gt;
# '''Administration Core'''&lt;br /&gt;
## Rachana Manandhar, Harvard (BWH SPL)&lt;br /&gt;
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)&lt;br /&gt;
&lt;br /&gt;
== NA-MIC Collaborators ==&lt;br /&gt;
These NA-MIC collaborators are funded under the &amp;quot;Collaboration with NCBC&amp;quot; PAR.&lt;br /&gt;
#Nicole Grosland, UIowa&lt;br /&gt;
#Vincent Magnotta, UIowa&lt;br /&gt;
#Steve Pieper, Isomics&lt;br /&gt;
#James Daunais, Wake Forest&lt;br /&gt;
#Robert Kraft, Wake Forest&lt;br /&gt;
#Chris Wyatt, Virginia Tech&lt;br /&gt;
#Kilian Pohl, Harvard (BWH SPL)&lt;br /&gt;
#Sandy Wells, Harvard (BWH SPL)&lt;br /&gt;
#Kevin Cleary, Georgetown&lt;br /&gt;
#Enrique Campos-Nanez, George Washington U.&lt;br /&gt;
#Patrick (Peng) Cheng, Georgetown&lt;br /&gt;
#Ziv Yaniv, Georgetown&lt;br /&gt;
#Nobuhiko Hata, Harvard (BWH)&lt;br /&gt;
#Curtis Lisle, KnowledgeVis&lt;br /&gt;
&lt;br /&gt;
==NA-MIC EAB==&lt;br /&gt;
&lt;br /&gt;
Our External Advisory Board members are listed [[EAB|here]].&lt;br /&gt;
&lt;br /&gt;
== NA-MIC alumni ==&lt;br /&gt;
* [http://marchingcubes.org Bill Lorensen]&lt;br /&gt;
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)&lt;br /&gt;
*  Lauren O'Donnell, MIT (CSAIL)&lt;br /&gt;
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)&lt;br /&gt;
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)&lt;br /&gt;
*  Ramsey Al-Hakim, Georgia Tech&lt;br /&gt;
*  [[User:Melonakos|John Melonakos]], Georgia Tech&lt;br /&gt;
*  [[User:Lankton|Shawn Lankton]], Georgia Tech&lt;br /&gt;
*  [[User:Nain|Delphine Nain]], Georgia Tech&lt;br /&gt;
*   Xavier Le Faucheur, Georgia Tech&lt;br /&gt;
*  [[User:Mohan|Vandana Mohan]], Georgia Tech&lt;br /&gt;
*  Tom Fletcher, Utah&lt;br /&gt;
*  [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah&lt;br /&gt;
*  [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah&lt;br /&gt;
* Josh Snyder, Harvard (MGH)&lt;br /&gt;
* [[User:DavidTuch|David Tuch]], Harvard (MGH)&lt;br /&gt;
* [[User:Karthik|Karthik Krishnan]],Kitware&lt;br /&gt;
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware&lt;br /&gt;
* [[User:Mathieu|Mathieu Malaterre]], Kitware&lt;br /&gt;
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA&lt;br /&gt;
* [[User:MichaelPan|Michael Pan]], UCLA&lt;br /&gt;
* Brendan Flaherty, UCSD&lt;br /&gt;
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)&lt;br /&gt;
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)&lt;br /&gt;
* Martha Shenton, Harvard (BWH PNL) PI&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI&lt;br /&gt;
* Bob Roth, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth&lt;br /&gt;
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth&lt;br /&gt;
* Alan Green, Dartmouth&lt;br /&gt;
* John West, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth&lt;br /&gt;
* Stephen Guerin, Dartmouth&lt;br /&gt;
* John MacDonald, Dartmouth&lt;br /&gt;
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI&lt;br /&gt;
* [[User:Jfallon|James Fallon]], UCI&lt;br /&gt;
* Jessica Turner, UCI&lt;br /&gt;
* Martina Panzenboeck, UCI&lt;br /&gt;
* David Medina, UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI&lt;br /&gt;
* Diane Highum, UCI&lt;br /&gt;
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI&lt;br /&gt;
* Liv Trondsen, UCI&lt;br /&gt;
* Fabio Macciardi, Toronto&lt;br /&gt;
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto&lt;br /&gt;
* Aristotle Voineskos, Toronto&lt;br /&gt;
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST&lt;br /&gt;
&lt;br /&gt;
== &amp;quot;Friends and Family&amp;quot; ==&lt;br /&gt;
&lt;br /&gt;
=== NIH ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Lysterp|Peter M. Lyster]]&lt;br /&gt;
&lt;br /&gt;
=== mBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Akolasny|Anthony Kolasny]]&lt;br /&gt;
* [[User:Dmarcus|Dan Marcus]]&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== fBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== IGT ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
* [[User:Noby| Nobuhiko Hata]]&lt;br /&gt;
&lt;br /&gt;
=== Other ===&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
	<entry>
		<id>https://www.na-mic.org/w/index.php?title=People&amp;diff=78068</id>
		<title>People</title>
		<link rel="alternate" type="text/html" href="https://www.na-mic.org/w/index.php?title=People&amp;diff=78068"/>
		<updated>2012-11-19T01:28:23Z</updated>

		<summary type="html">&lt;p&gt;Tannenba: /* Personnel at least partially funded by NA-MIC */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Personnel at least partially funded by NA-MIC ==&lt;br /&gt;
&lt;br /&gt;
#'''Leadership Core'''&lt;br /&gt;
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Naucoin|Nicole Aucoin]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)&lt;br /&gt;
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)&lt;br /&gt;
## Wendy Plesniak, Harvard (BWH SPL) &lt;br /&gt;
# '''Algorithms Core'''&lt;br /&gt;
## University of Utah C1&lt;br /&gt;
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''&lt;br /&gt;
### Manasi Datar&lt;br /&gt;
### Samuel Gerber&lt;br /&gt;
### Yongshen Pan&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''&lt;br /&gt;
### Emmanuel Bitaud&lt;br /&gt;
### Stanley Dirrleman&lt;br /&gt;
### James Fishbaugh&lt;br /&gt;
### Marcel Prastawa&lt;br /&gt;
### [[User:Gcasey|Casey Goodlett]]&lt;br /&gt;
### Preston T. Fletcher&lt;br /&gt;
### Ran Tao&lt;br /&gt;
## MIT (CSAIL)&lt;br /&gt;
### '''[[Polina_Golland|Polina Golland]], PI'''&lt;br /&gt;
### [[Eric_Grimson|Eric Grimson]]&lt;br /&gt;
###  [[User:FernD|Fern DeOliveira]]&lt;br /&gt;
### Michal Depa&lt;br /&gt;
### Tammy Riklin Raviv&lt;br /&gt;
## UNC&lt;br /&gt;
### '''[[User:Styner|Martin Styner]], PI'''&lt;br /&gt;
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]&lt;br /&gt;
### Michael Garret Larson&lt;br /&gt;
### Gary Long&lt;br /&gt;
### Deepika Mahalingam&lt;br /&gt;
### Ipek Oguz&lt;br /&gt;
### Rachel Gimpel Smith&lt;br /&gt;
### Clement Vachet&lt;br /&gt;
## Boston University/UAB&lt;br /&gt;
###'''[[User:Tannenba|Allen Tannenbaum|]], PI'''&lt;br /&gt;
### [http://http://iss.bu.edu/tannenba/]&lt;br /&gt;
### Yi Gao&lt;br /&gt;
### Arie Nakhmani&lt;br /&gt;
### Ivan Kolesov&lt;br /&gt;
### Peter Karasev&lt;br /&gt;
## MGH &lt;br /&gt;
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]&lt;br /&gt;
## Kitware, Inc.&lt;br /&gt;
###'''[[User:Will| Will Schroeder]],  PI'''&lt;br /&gt;
### [[User:Barre|Sebastien Barre]]&lt;br /&gt;
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]&lt;br /&gt;
### [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
## GE&lt;br /&gt;
### '''[[User:Millerjv|Jim Miller]], PI'''&lt;br /&gt;
### Roshni Bhagalia&lt;br /&gt;
### Dirk Padfield&lt;br /&gt;
### James Ross&lt;br /&gt;
### [[User:Taox|Xiaodong Tao]]&lt;br /&gt;
### [[User:Harveerar|Harini Veeraraghavan]]&lt;br /&gt;
### [[User:kedar_p|Kedar Patwardhan]]&lt;br /&gt;
## Isomics&lt;br /&gt;
### '''[[User:Pieper|Steve Pieper]], PI'''&lt;br /&gt;
### Alex Yarmakovich&lt;br /&gt;
## WUSTL&lt;br /&gt;
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''&lt;br /&gt;
### Kevin Archie&lt;br /&gt;
### Mikhail Milchenko&lt;br /&gt;
### Timothy Olsen&lt;br /&gt;
## UCSD&lt;br /&gt;
### '''Jeffrey S. Grethe, PI'''&lt;br /&gt;
### Mark Ellisman&lt;br /&gt;
### Marco Ruiz&lt;br /&gt;
# '''Driving Biological Projects (DBP)'''&lt;br /&gt;
## University of Utah&lt;br /&gt;
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''&lt;br /&gt;
### Josh Blauer&lt;br /&gt;
### Josh Cates&lt;br /&gt;
## Iowa&lt;br /&gt;
### '''Hans Johnson, PI'''&lt;br /&gt;
### Norman Williams&lt;br /&gt;
### [[User:Dmwelch | Dave Welch]]&lt;br /&gt;
### Eun Young &amp;quot;Regina&amp;quot; Kim&lt;br /&gt;
### Joy Matsui&lt;br /&gt;
### Ali Ghayhoor&lt;br /&gt;
### Chen Yang&lt;br /&gt;
## MGH&lt;br /&gt;
### '''Gregory C. Sharp, PI'''&lt;br /&gt;
### Rui Li&lt;br /&gt;
## UCLA&lt;br /&gt;
### '''Jack Van Horn, PI'''&lt;br /&gt;
### Jeffrey Alger&lt;br /&gt;
### Ian Bowman&lt;br /&gt;
### Celia Cheung&lt;br /&gt;
### Nathan Hageman&lt;br /&gt;
### David Hovda&lt;br /&gt;
### Arthur W. Toga&lt;br /&gt;
### Paul Vespa&lt;br /&gt;
## Queen's University&lt;br /&gt;
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''&lt;br /&gt;
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]&lt;br /&gt;
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]&lt;br /&gt;
### Siddharth Vikal&lt;br /&gt;
## The Mind Institute&lt;br /&gt;
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''&lt;br /&gt;
### Mark Scully&lt;br /&gt;
## BWH&lt;br /&gt;
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''&lt;br /&gt;
### Jorge Alvarado&lt;br /&gt;
### Jennifer Goodrich&lt;br /&gt;
### Padmapriya Srinivazan&lt;br /&gt;
# '''Service Core'''&lt;br /&gt;
## Kitware, Inc. &lt;br /&gt;
### '''[[User:Will|Will Schroeder]], PI'''&lt;br /&gt;
### Zack Galbreath&lt;br /&gt;
### William Hoffman&lt;br /&gt;
### Julien Jomier&lt;br /&gt;
### Zach Mullen&lt;br /&gt;
# '''Training Core'''&lt;br /&gt;
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''&lt;br /&gt;
## [[User:Randy|Randy Gollub]], Harvard (MGH) PI&lt;br /&gt;
# '''Dissemination Core'''&lt;br /&gt;
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''&lt;br /&gt;
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''&lt;br /&gt;
# '''Administration Core'''&lt;br /&gt;
## Rachana Manandhar, Harvard (BWH SPL)&lt;br /&gt;
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)&lt;br /&gt;
&lt;br /&gt;
== NA-MIC Collaborators ==&lt;br /&gt;
These NA-MIC collaborators are funded under the &amp;quot;Collaboration with NCBC&amp;quot; PAR.&lt;br /&gt;
#Nicole Grosland, UIowa&lt;br /&gt;
#Vincent Magnotta, UIowa&lt;br /&gt;
#Steve Pieper, Isomics&lt;br /&gt;
#James Daunais, Wake Forest&lt;br /&gt;
#Robert Kraft, Wake Forest&lt;br /&gt;
#Chris Wyatt, Virginia Tech&lt;br /&gt;
#Kilian Pohl, Harvard (BWH SPL)&lt;br /&gt;
#Sandy Wells, Harvard (BWH SPL)&lt;br /&gt;
#Kevin Cleary, Georgetown&lt;br /&gt;
#Enrique Campos-Nanez, George Washington U.&lt;br /&gt;
#Patrick (Peng) Cheng, Georgetown&lt;br /&gt;
#Ziv Yaniv, Georgetown&lt;br /&gt;
#Nobuhiko Hata, Harvard (BWH)&lt;br /&gt;
#Curtis Lisle, KnowledgeVis&lt;br /&gt;
&lt;br /&gt;
==NA-MIC EAB==&lt;br /&gt;
&lt;br /&gt;
Our External Advisory Board members are listed [[EAB|here]].&lt;br /&gt;
&lt;br /&gt;
== NA-MIC alumni ==&lt;br /&gt;
* [http://marchingcubes.org Bill Lorensen]&lt;br /&gt;
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)&lt;br /&gt;
*  Lauren O'Donnell, MIT (CSAIL)&lt;br /&gt;
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)&lt;br /&gt;
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)&lt;br /&gt;
*  Ramsey Al-Hakim, Georgia Tech&lt;br /&gt;
*  [[User:Melonakos|John Melonakos]], Georgia Tech&lt;br /&gt;
*  [[User:Lankton|Shawn Lankton]], Georgia Tech&lt;br /&gt;
*  [[User:Nain|Delphine Nain]], Georgia Tech&lt;br /&gt;
*   Xavier Le Faucheur, Georgia Tech&lt;br /&gt;
*  [[User:Mohan|Vandana Mohan]], Georgia Tech&lt;br /&gt;
*  Tom Fletcher, Utah&lt;br /&gt;
*  [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah&lt;br /&gt;
*  [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah&lt;br /&gt;
* Josh Snyder, Harvard (MGH)&lt;br /&gt;
* [[User:DavidTuch|David Tuch]], Harvard (MGH)&lt;br /&gt;
* [[User:Karthik|Karthik Krishnan]],Kitware&lt;br /&gt;
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware&lt;br /&gt;
* [[User:Mathieu|Mathieu Malaterre]], Kitware&lt;br /&gt;
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA&lt;br /&gt;
* [[User:MichaelPan|Michael Pan]], UCLA&lt;br /&gt;
* Brendan Flaherty, UCSD&lt;br /&gt;
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)&lt;br /&gt;
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)&lt;br /&gt;
* Martha Shenton, Harvard (BWH PNL) PI&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)&lt;br /&gt;
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI&lt;br /&gt;
* Bob Roth, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth&lt;br /&gt;
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth&lt;br /&gt;
* Alan Green, Dartmouth&lt;br /&gt;
* John West, Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth&lt;br /&gt;
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth&lt;br /&gt;
* Stephen Guerin, Dartmouth&lt;br /&gt;
* John MacDonald, Dartmouth&lt;br /&gt;
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI&lt;br /&gt;
* [[User:Jfallon|James Fallon]], UCI&lt;br /&gt;
* Jessica Turner, UCI&lt;br /&gt;
* Martina Panzenboeck, UCI&lt;br /&gt;
* David Medina, UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI&lt;br /&gt;
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI&lt;br /&gt;
* Diane Highum, UCI&lt;br /&gt;
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI&lt;br /&gt;
* Liv Trondsen, UCI&lt;br /&gt;
* Fabio Macciardi, Toronto&lt;br /&gt;
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto&lt;br /&gt;
* Aristotle Voineskos, Toronto&lt;br /&gt;
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST&lt;br /&gt;
&lt;br /&gt;
== &amp;quot;Friends and Family&amp;quot; ==&lt;br /&gt;
&lt;br /&gt;
=== NIH ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Lysterp|Peter M. Lyster]]&lt;br /&gt;
&lt;br /&gt;
=== mBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Akolasny|Anthony Kolasny]]&lt;br /&gt;
* [[User:Dmarcus|Dan Marcus]]&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== fBIRN ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Kikinis|Ron Kikinis]]&lt;br /&gt;
&lt;br /&gt;
=== IGT ===&lt;br /&gt;
&lt;br /&gt;
* [[User:Ibanez|Luis Ibanez]]&lt;br /&gt;
* [[User:Noby| Nobuhiko Hata]]&lt;br /&gt;
&lt;br /&gt;
=== Other ===&lt;/div&gt;</summary>
		<author><name>Tannenba</name></author>
		
	</entry>
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