NAMIC Wiki - User contributions [en]

2010 Winter Project Week Tractography

2010-01-08T15:46:15Z

Malcolm: status update

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:Filtered_tractography.png|Left hemisphere
</gallery>

==Key Investigators==
* BWH: James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing. Picking fibers and moving them between polydata structures.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have Python/NumPy implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.) and deterministic tractography infrastructure.

However, it is unusably slow (the MATLAB version runs faster). Profiling the code seems to indicate that there is too much NumPy overhead in manipulating lots of small matrices/vectors. Now reimplementing in C/C++.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
# Savadjiev, Zucker, Siddiqi. "On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis", ICCV 2007
</div>

2010 Winter Project Week Tractography

2010-01-04T19:29:28Z

Malcolm:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:Filtered_tractography.png|Left hemisphere
</gallery>

==Key Investigators==
* BWH: James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing. Picking fibers and moving them between polydata structures.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
# Savadjiev, Zucker, Siddiqi. "On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis", ICCV 2007
</div>

2010 Winter Project Week Tractography

2010-01-04T16:39:42Z

Malcolm:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>[[File:Filtered_tractography.png|600px]]

==Key Investigators==
* BWH: James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing. Picking fibers and moving them between polydata structures.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
# Savadjiev, Zucker, Siddiqi. "On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis", ICCV 2007
</div>

File:Filtered tractography.png

2010-01-04T16:36:31Z

Malcolm: Cutaway showing filtered two-tensor tractography of the left hemisphere colored with fractional anisotropy. Several major structures are visible including the cingulum bundle and fornix in addition to overall dense cortical insertion.

Cutaway showing filtered two-tensor tractography of the left hemisphere colored with fractional anisotropy. Several major structures are visible including the cingulum bundle and fornix in addition to overall dense cortical insertion.

User:Malcolm

2009-12-03T22:21:22Z

Malcolm: Created page with 'http://pnl.bwh.harvard.edu/people/profiles/malcolm.html http://www.bme.gatech.edu/groups/bil/people/James.Malcolm/James.Malcolm.html http://www.jgmalcolm.com'

http://pnl.bwh.harvard.edu/people/profiles/malcolm.html

http://www.bme.gatech.edu/groups/bil/people/James.Malcolm/James.Malcolm.html

http://www.jgmalcolm.com

2010 Winter Project Week Tractography

2009-12-03T22:18:53Z

Malcolm:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing. Picking fibers and moving them between polydata structures.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
# Savadjiev, Zucker, Siddiqi. "On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis", ICCV 2007
</div>

2010 Winter Project Week Tractography

2009-12-02T01:27:13Z

Malcolm: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Savadjiev, Campbell, Pike, Siddiqi. "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week Tractography

2009-12-02T01:26:34Z

Malcolm: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Approach, Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Savadjiev, Campbell, Pike, Siddiqi. "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week Tractography

2009-12-02T01:25:52Z

Malcolm: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Approach, Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Savadjiev, Campbell, Pike, Siddiqi. "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi. "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week Tractography

2009-12-02T01:24:09Z

Malcolm: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Approach, Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week Tractography

2009-12-02T01:21:53Z

Malcolm: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Approach, Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography.
</div>

<p>
<div style="width: 97%; float: left; padding-right: 3%">
# Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week Tractography

2009-12-02T01:21:19Z

Malcolm: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Approach, Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding.</div>

<div style="width: 27%; float: left; padding-right: 3%">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography.
</div>
<br>
<div style="width: 97%">
# Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week Tractography

2009-12-02T01:19:28Z

Malcolm:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding.</div>

<div style="width: 40%; float: left;">
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography.
</div>
</div>

<div style="width: 97%; float: left;">
# Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
# Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
</div>

2010 Winter Project Week

2009-12-02T00:50:54Z

Malcolm: /* Diffusion */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([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], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Steve Pieper)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)

=== IGT ===
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker)

=== Radiotherapy ===

=== Analysis ===

=== Informatics ===

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin)

=== Python ===

=== NA-MIC Kit Internals ===
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)

=== Execution Model ===

=== Preparation ===

# 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]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[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.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). 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.)
## 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. (Zack)
# 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...

2010 Winter Project Week

2009-12-02T00:50:30Z

Malcolm: /* Diffusion */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([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], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Steve Pieper)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)

=== IGT ===
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker)

=== Radiotherapy ===

=== Analysis ===

=== Informatics ===

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Integration of model-based filtered tractography]] (Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin)

=== Python ===

=== NA-MIC Kit Internals ===
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)

=== Execution Model ===

=== Preparation ===

# 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]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[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.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). 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.)
## 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. (Zack)
# 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...

2010 Winter Project Week

2009-12-02T00:49:54Z

Malcolm: /* Diffusion */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([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], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Steve Pieper)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)

=== IGT ===
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker)

=== Radiotherapy ===

=== Analysis ===

=== Informatics ===

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Integration of model-based filtered tractography]] (Peter Savadjiev, James Malcolm, C-F Westin, Yogesh Rathi)

=== Python ===

=== NA-MIC Kit Internals ===
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)

=== Execution Model ===

=== Preparation ===

# 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]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[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.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). 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.)
## 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. (Zack)
# 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...

2010 Winter Project Week Tractography

2009-12-02T00:47:36Z

Malcolm: Created page with '__NOTOC__ <gallery> Image:PW-SLC2010.png|Projects List </gallery> ==Key Investigators== * UPenn: Luke Bloy, Ragini Verma * BWH: Carl-Fredri…'

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* UPenn: Luke Bloy, Ragini Verma
* BWH: Carl-Fredrik Westin

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We would like to provide support for high angular resolution diffusion imaging (HARDI) data models which make use of the symmetric real spherical harmonic functions (RSH) as a basis for functions on the sphere.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
First consensus must be reached on the exact form of the RSH basis to be used. This will provide a basis for future development.

The functionality we would like to provide is the following:
# MRML representations for images of RSH coefficients
# Visualization of images of RSH coefficients
# Routines for estimating the orientation distribution function (ODF) (Descoteaux2007)
# Routines for estimating the fiber orientation distribution (FOD) (Tournier2007) using both filtered and constrained spherical deconvolution.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

We have implemented the RSH basis and RSH coefficients as ITK based C++ classes, and written ITK image filters to perform the
model estimation. Slicer modules need to be written to perform the integrated these filters into the Slicer framework.

MRML nodes have been written for RSH volumes. These nodes were based (blindly) off of the DiffusionTensor MRML nodes. There is a mathematics class
which computes scalar maps based off of the RSH coefficients, as well as glpyher which currently only supports sphere sources. Support for line glyph source, to show only the principle diffusion directions would be very beneficial since rendering all the points on the sphere source is very resource intensive.

Visualization is currently being achieved by an extension to the Volumes module in slicer. The display widget nodes to facilitate this were again based on the Diffusion Widget classes.

</div>
</div>

<div style="width: 97%; float: left;">

==References==
*Maxime Descoteaux, Elaine Angelino, Shaun Fitzgibbons, and Rachid Deriche, “Regularized, fast, and robust analytical q-ball imaging,” Magnetic Resonance in Medicine, vol. 58, no. 3, pp. 497–510, 2007.
*J-Donald Tournier, Fernando Calamante, and Alan Connelly, “Robust determination of the fibre orientation distribution in diffusion mri: Non-negativity constrained super-resolved spherical deconvolution,” NeuroImage, vol. 35, no. 4, pp. 1459–1472, May 2007.

</div>

2010 Winter Project Week

2009-12-02T00:47:15Z

Malcolm: /* Diffusion */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([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], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Steve Pieper)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)

=== IGT ===
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker)

=== Radiotherapy ===

=== Analysis ===

=== Informatics ===

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Integration of model-based filtered tractography]] (Peter Savadjiev, James Malcolm)

=== Python ===

=== NA-MIC Kit Internals ===
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)

=== Execution Model ===

=== Preparation ===

# 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]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[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.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). 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.)
## 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. (Zack)
# 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...

2010 Winter Project Week

2009-12-02T00:46:54Z

Malcolm: added tractography

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([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], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Steve Pieper)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)

=== IGT ===
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker)

=== Radiotherapy ===

=== Analysis ===

=== Informatics ===

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models ]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Integration of model-based filtered tractography ]] (Peter Savadjiev, James Malcolm)

=== Python ===

=== NA-MIC Kit Internals ===
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)

=== Execution Model ===

=== Preparation ===

# 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]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[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.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). 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.)
## 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. (Zack)
# 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...

AHM 2010

2009-11-12T21:35:36Z

Malcolm: added my name

__NOTOC__
== Introduction ==
{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2" align="left" | '''This is the home page for the 2010 NA-MIC all hands meeting (AHM).''' NA-MIC participants meet for a AHM once a year. The purpose of the AHM is to coordinate, discuss plans and report to NIH officers and the external advisory board (EAB). The external advisory board meets with the NA-MIC leadership immediately after the AHM. In parallel, NA-MIC is organizing a project week. These events, with the exception of the EAB meeting, are open to collaborators and potential collaborators.

For more information about the project weeks in general, click [[Engineering:Programming_Events|'''here''']].

For information about the January 2010 project week, see below or click [[2010_Winter_Project_Week|'''here''']].

For information about Utah as a travel destination click [http://www.utah.com '''here'''].
| style="background: #ebeced" align="center"| [[Image:SLC.jpg|center|350px|View of the City]]
|-
| style="background: #ebeced"|The 2010 AHM, EAB and Project Week will be held <br>'''January 4-8 2010''', in '''Salt Lake City''', Utah.
|}

== Agenda==

'''wireless connection: capital-ballroom, namic-ballroomB, namic-amethyst'''<br>

{| border="1" cellpadding="5"
|- style="background:#ebeced; color:black" align="left"
| style="width:4%" | '''Time'''
| style="width:12%" | '''Monday, January 4'''
| style="width:12%" | '''Tuesday, January 5'''
| style="width:12%" | '''Wednesday, January 6'''
| style="width:32%" | '''Thursday, January 7 '''
| style="width:12%" | '''Friday, January 8'''
|-
| style="background:#ffffdd; color:black"|
| style="background:#b4d597; color:#522200"| '''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#b4d597; color:#522200"| '''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#b4d597; color:#522200"| '''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#fff6a6; color:#522200"| '''AHM''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/Capitol A-B], [[2008_EAB|'''EAB''']] in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus B]
'''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus A]
| style="background:#b4d597; color:#522200"|'''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
|-
| style="background:#ffffdd; color:black"|'''7:30-8:00'''
| style="background:#ebeced; color:black"|
| style="background:#b4d597; color:black"| Breakfast
| style="background:#b4d597; color:black"| Breakfast
| style="background:#fff6a6; color:black"| Breakfast
| style="background:#b4d597; color:black"| Breakfast
|-
| style="background:#ffffdd; color:black"|'''8:00-10:00'''
| style="background:#ebeced; color:black"|'''9:30''' Core 1 and 2 PI closed session in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol A]
| style="background:#b4d597; color:black"|Project Work <br> '''8:00-9:00''' Talk <br>
'''9:00-10:00''' Talk in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"|Project Work <br>'''8:00-9:00''' [[AHM2010:Tutorial Contest|Tutorial Contest]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
'''9:00-10:00''' Breakout (Capital Ballroom A)
| style="background:#fff6a6; color:black"|'''8:00''' [[AHM 2010 Introduction|Introduction]], [http://www.spl.harvard.edu/~kikinis Ron Kikinis] <br>
'''8:05''' [[AHM 2010 NA-MIC Highlights|NA-MIC Highlights]] ([http://www.cs.utah.edu/~whitaker/ Ross Whitaker])<br>
'''8:20''' [[AHM 2010 NA-MIC Validation|Validation]] ([http://www.cs.unc.edu/~styner/ Martin Styner])<br>
'''Roadmap Projects'''<br>
'''8:35''': [[AHM2010:JHU|JHU/Queens]] ([http://research.cs.queensu.ca/~gabor/ Gabor Fichtinger])<br>
'''8:55''': [[AHM2010:UNC|UNC]] ([http://www.med.unc.edu/psych/directories/faculty/hazlett/ Heather Cody])<br>
'''9:15''': [[AHM2010:PNL|PNL]] ([http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki])<br>
'''9.35''': [[AHM2010:Mind|Mind Institute]] ([http://www.mrn.org/principle-investigators/h.-jeremy-bockholt.html Jeremy Bockolt])<br>
| style="background:#b4d597; color:black"|Project Work <br>
|-
| style="background:#ffffdd; color:black"|'''10:00-10:30'''
| style="background:#ebeced; color:black"| Core 1 and 2 PI closed session
| style="background:#b4d597; color:black"| '''10:30-11:30'''Breakout Session [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"| Coffee
| style="background:#fff6a6; color:black"| Coffee
| style="background:#b4d597; color:black"| '''10:00''' Project Review
|-
| style="background:#ffffdd; color:black"|'''10:30-12:00'''
| style="background:#ebeced; color:black"| Core 1 and 2 PI closed session
| style="background:#b4d597; color:black"|Project Work
| style="background:#b4d597; color:black"|Project Work <br>

| style="background:#fff6a6; color:black"|
'''Collaborations'''<br>
'''10:30''': Iowa ([http://www.engineering.uiowa.edu/faculty-staff/profile-directory/bme/grosland_n.php Nicole Grosland])<br>
'''10:40''': Wake Forest ([http://www.ece.vt.edu/faculty/wyatt.html Chris Wyatt])<br>
'''10:50''': Georgetown ([http://www.isis.georgetown.edu/PORTALVBVS/DesktopDefault.aspx?tabindex=2&tabid=8 Kevin Cleary])<br>
'''11:00''': UNC ([http://www.med.unc.edu/~dgshen Dinggang Shen])<br>
'''11:10''': JHU (Jerry Prince)<br>
'''11:20''': MGH (Hiroyuki Yoshida)<br>
'''11:30''': JHU (Michael Miller) <br>
'''11:40''': Utah (Janet Lainhart) <br>
'''11:50''': Mario Negri Collaboration ([http://villacamozzi.marionegri.it/~luca/ Luca Antiga])<br>
'''12:00''': Discussion<br>
| style="background:#b4d597; color:black"|Project Work
|-
| style="background:#ffffdd; color:black"|'''12:00-1:00'''
| style="background:#b4d597; color:black"| Lunch
| style="background:#b4d597; color:black"| Lunch <br>
| style="background:#b4d597; color:black"| Lunch<br>
| style="background:#fff6a6; color:black"| Lunch
| style="background:#b4d597; color:black"| Adjourn
|-
| style="background:#ffffdd; color:black"|'''1:00-3:00'''
| style="background:#b4d597; color:black"|[[2010_Winter_Project_Week|Begin Project Activities]]: Introduce Projects and Participants
| style="background:#b4d597; color:black"|Breakout [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/Amethyst 1]
| style="background:#b4d597; color:black"|Project Work (Capital Ballroom A) <br> '''1:00-2:00''' Breakout <br> '''2:00-3:00''' Breakout
| style="background:#fff6a6; color:black"|
'''Tools and Tutorials''' <br>
'''1:00-1:20''' [[AHM2010:Slicer|Slicer]] ([http://www.spl.harvard.edu/~pieper Steve Pieper])<br>
'''1:20-1:40''' [[AHM2010:Plug-ins|Interfacing with Slicer]] ([http://wiki.na-mic.org/Wiki/index.php/User:Millerjv Jim Miller])<br>
'''1:40-2:00''' [[AHM2010:Non-interactive-tools|Non-interactive tools]] ([http://www.kitware.com/company/team/aylward.html Stephen Aylward])<br>
'''2:00-2:30''' Training Core Update ([http://lmi.bwh.harvard.edu/~spujol/ Sonia Pujol]) <br>
'''2:30-3:00''' [[AHM2010:Tutorial-Contest-Winners|Tutorial Contest]]: Presentations by the winners ([http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=64 Randy Gollub])<br>
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''3:00-3:30'''
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#fff6a6; color:black"| Coffee
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''3:00-5:00'''
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|'''3:30-4:30''' Breakout <br> Project Work <br>[http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"|Project Work <br>'''3:00-5:00''' [[AHM2010:SlicerHandson|Slicer Hands-on with Ron]]<br>Breakout<br>[http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#fff6a6; color:black"|[[2008 EAB|EAB]]<br>[http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus B]<br>'''3:00-4:00''' Discussion with NA-MIC Leadership<br> '''4:00-5:00''' Closed Session
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''05:00-07:00'''
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|'''05:00-06:00''' Breakout(Capital Ballroom A)
| style="background:#fff6a6; color:black"|'''6:00''' Optional: [http://www.skisaltlake.com/murphys.htm Beer at Murphy's] (like last year)
| style="background:#ebeced; color:black"|
|}

Please note that there will be a Core 1&2 Site PI Retreat on the morning of Monday, January 4th. This is a closed session for Core 1&2 Site PIs, with no delegates. The topic is the competitive renewal.

== Dates.Venue.Registration ==
'''Dates:'''
* The All Hands Meeting and External Advisory Board Meeting will be held on '''Thursday, January 7th'''.
* Project Activities will be held rest of the week between '''Monday, January 4th and Friday, January 8th'''.

'''Venue:''' The venue for the meeting is [http://www.marriott.com/hotels/travel/slccc-salt-lake-city-marriott-city-center/ Marriott City Center, Salt Lake City, Utah] Marriott City Center, Salt Lake City, Utah. [http://marriott.com/property/meetingsandevents/floorplans/slccc (Floorplan)]. Please either call the hotel at +1-877-905-4491 (toll free) or book online at http://www.marriott.com/hotels/travel/slccc-salt-lake-city-marriott-city-center/?toDate=1/8/10&groupCode=NAMNAMA&fromDate=1/3/10&app=resvlink'''by December 4, 2009''' using the code NAMNAMA to get rooms at $139/night. Please note that we do need attendees to use this hotel in order to not incur additional charges for the use of conference rooms. Please also note that the room rate without the code is ~$200/night and we will not be able to help you get a discount if you don't book in time.

'''Registration:''' We are charging a registration fee to all participants ($200 for AHM only, and $450 for AHM+). The fee covers the costs of the facilities and food provided. In order to keep the fee low, we need to get a sufficient number of hotel nights by our participants. See above for more on this. Please click http://www.sci.utah.edu/namic2010.html for online registration. This registration must be completed by December 12, 2009.<br>

'''Connectivity'''
We have three wireless access points at the AHM. Two of them are located in the capital ballroom. One is named capital-ballroom, the other is named capital-ballroom2. If one access point doesn't let you connect it is probably overloaded. In that case, please try connecting to the other one.

== Attendees ==

The registered attendee list will be posted here by the organizers.
#Ron Kikinis, BWH
#Katie Mastrogiacomo, BWH
#Sonia Pujol, BWH
#Nicole Aucoin, BWH
#Katie Hayes, BWH
#Lauren O'Donnell, BWH
#Sandy Wells, BWH
#Andrew Rausch, PNL
#Alexander Zaitsev, BWH
#Andriy Fedorov, BWH
#Raul San Jose Estepar, BWH (AHM only)
#Wendy Plesniak, BWH
#Lauren O'Donnell, BWH
#Peter Savadjiev, BWH
#Petter Risholm, BWH
#Dominik Meier, BWH
#Junichi Tokuda, BWH
#Scott Hoge, BWH
#Ben Schwartz, BWH
#Marek Kubicki, BWH
#Sylvain Bouix, BWH
#Sylvain Jaume, MIT
#James Malcolm, BWH

Projects:LabelSpace

2009-06-27T06:40:17Z

Malcolm: added MICCAI reference

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

Two key aspects of coupled multi-object shape analysis are the choice of
representation and subsequent registration to align the sample set. Current
techniques for such analysis tend to trade off performance between the two
tasks, performing well for one task but developing problems when used for
the other. We propose label space, a representation that is both flexible
and well suited for both tasks. Under this framework, object labels are
mapped to vertices of a regular simplex, e.g. the unit interval for two
labels, a triangle for three labels, a tetrahedron for four labels, etc.
This forms a linear space with the property that all labels are equally
spaced. On examination, this representation has several desirable properties:
algebraic operations may be done directly, label uncertainty is expressed as
a weighted mixture of labels, interpolation is unbiased toward any label or
the background, and registration may be performed directly. To demonstrate these properties, we describe variational registration
directly in this space. Many registration methods fix one of the maps and
align the rest of the set to this fixed map. To remove the bias induced by
arbitrary selection of the fixed map, we align a set of label maps to their
intrinsic mean map.

[[Image:Label_space.png|thumb|Figure 1: The first three label space configurations: a unit interval for two labels, a triangle for three labels, and a tetrahedron for four labels (left to right). ]]
[[Image:Ls_original_aligned.png|thumb|Figure 2: Alignment of a set of 30 maps used in the study by Tsai et al. (2003). The original and aligned sets are superimposed for visualization.]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Coupled Multi-Shape Representation." In MICCAI, 2008.

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

Algorithm:GATech

2009-06-27T06:39:06Z

Malcolm: /* Label Space: A Coupled Multi-Shape Representation */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, 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.

= Georgia Tech Projects =

{| cellpadding="10"

| style="width:15%" | [[Image:Results brain sag.JPG|200px|]]
| style="width:85%" |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly 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...]]

<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Segmentation tool put into Slicer3.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

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...]]

<font color="red">'''New: '''</font> Will be put into Slicer3.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

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 "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

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...]]

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

|-

| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> 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.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Bayesian Spherical Wavelet Shrinkage:Applications to shape analysis, X. Le Faucheur, B. Vidakovic, A. Tannenbaum, Proc. of SPIE Optics East, 2007.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

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...]]

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Coupled Multi-Shape Representation." In MICCAI, 2008.

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

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...]]

<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. To be published in Macromolecules. 2008.

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

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...]]

<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

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...]]

<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

<font color="red">'''New: '''</font> Currently under investigation.

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

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...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2009-06-27T06:38:48Z

Malcolm: /* Label Space: A Coupled Multi-Shape Representation */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, 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.

= Georgia Tech Projects =

{| cellpadding="10"

| style="width:15%" | [[Image:Results brain sag.JPG|200px|]]
| style="width:85%" |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly 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...]]

<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Segmentation tool put into Slicer3.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

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...]]

<font color="red">'''New: '''</font> Will be put into Slicer3.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

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 "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

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...]]

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

|-

| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> 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.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Bayesian Spherical Wavelet Shrinkage:Applications to shape analysis, X. Le Faucheur, B. Vidakovic, A. Tannenbaum, Proc. of SPIE Optics East, 2007.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

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...]]

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Coupled Multi-Shape Representation." In MICCAI, 2008.

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

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...]]

<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. To be published in Macromolecules. 2008.

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

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...]]

<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

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...]]

<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

<font color="red">'''New: '''</font> Currently under investigation.

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

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...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2009-06-27T06:38:34Z

Malcolm: added MICCAI reference, dropped "New"

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, 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.

= Georgia Tech Projects =

{| cellpadding="10"

| style="width:15%" | [[Image:Results brain sag.JPG|200px|]]
| style="width:85%" |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly 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...]]

<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Segmentation tool put into Slicer3.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

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...]]

<font color="red">'''New: '''</font> Will be put into Slicer3.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

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 "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

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...]]

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

|-

| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> 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.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Bayesian Spherical Wavelet Shrinkage:Applications to shape analysis, X. Le Faucheur, B. Vidakovic, A. Tannenbaum, Proc. of SPIE Optics East, 2007.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

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...]]

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Coupled Multi-Shape Representation." In MICCAI, 2008.
J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

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...]]

<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. To be published in Macromolecules. 2008.

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

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...]]

<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

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...]]

<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

<font color="red">'''New: '''</font> Currently under investigation.

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

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...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:LabelSpace

2008-04-24T17:13:26Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

Two key aspects of coupled multi-object shape analysis are the choice of
representation and subsequent registration to align the sample set. Current
techniques for such analysis tend to trade off performance between the two
tasks, performing well for one task but developing problems when used for
the other. We propose label space, a representation that is both flexible
and well suited for both tasks. Under this framework, object labels are
mapped to vertices of a regular simplex, e.g. the unit interval for two
labels, a triangle for three labels, a tetrahedron for four labels, etc.
This forms a linear space with the property that all labels are equally
spaced. On examination, this representation has several desirable properties:
algebraic operations may be done directly, label uncertainty is expressed as
a weighted mixture of labels, interpolation is unbiased toward any label or
the background, and registration may be performed directly. To demonstrate these properties, we describe variational registration
directly in this space. Many registration methods fix one of the maps and
align the rest of the set to this fixed map. To remove the bias induced by
arbitrary selection of the fixed map, we align a set of label maps to their
intrinsic mean map.

[[Image:Label_space.png|thumb|Figure 1: The first three label space configurations: a unit interval for two labels, a triangle for three labels, and a tetrahedron for four labels (left to right). ]]
[[Image:Ls_original_aligned.png|thumb|Figure 2: Alignment of a set of 30 maps used in the study by Tsai et al. (2003). The original and aligned sets are superimposed for visualization.]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

Projects:LabelSpace

2008-04-24T17:12:56Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

Two key aspects of coupled multi-object shape analysis are the choice of
representation and subsequent registration to align the sample set. Current
techniques for such analysis tend to trade off performance between the two
tasks, performing well for one task but developing problems when used for
the other.

We propose label space, a representation that is both flexible
and well suited for both tasks. Under this framework, object labels are
mapped to vertices of a regular simplex, e.g. the unit interval for two
labels, a triangle for three labels, a tetrahedron for four labels, etc.
This forms a linear space with the property that all labels are equally
spaced.

On examination, this representation has several desirable properties:
algebraic operations may be done directly, label uncertainty is expressed as
a weighted mixture of labels, interpolation is unbiased toward any label or
the background, and registration may be performed directly.

To demonstrate these properties, we describe variational registration
directly in this space. Many registration methods fix one of the maps and
align the rest of the set to this fixed map. To remove the bias induced by
arbitrary selection of the fixed map, we align a set of label maps to their
intrinsic mean map.

[[Image:Label_space.png|thumb|Figure 1: The first three label space configurations: a unit interval for two labels, a triangle for three labels, and a tetrahedron for four labels (left to right). ]]
[[Image:Ls_original_aligned.png|thumb|Figure 2: Alignment of a set of 30 maps used in the study by Tsai et al. (2003). The original and aligned sets are superimposed for visualization.]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

Projects:KPCA LLE KLLE ShapeAnalysis

2008-04-24T17:10:57Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= KPCA LLE KLLE Shape Analysis =

Our Objective is to compare various shape representation techniques like linear PCA (LPCA), kernel PCA (KPCA), locally linear embedding (LLE) and
kernel locally linear embedding (KLLE).

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Table1.png|thumb|600px|Figure 1: Table gives the number of mislabelled voxels for each of the methods for left caudate nucleus]]
[[Image:Table2.png|thumb|600px|Figure 2: Table gives the number of mislabelled voxels for each of the methods for left hippocampus]]

= Key Investigators =

* Georgia Tech Algorithms: Yogesh Rathi, Samuel Dambreville, Allen Tannenbaum

= Publications =

''In Print''
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3AKPCA+LLE+KLLE+ShapeAnalysis&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]

[[Category: Shape Analysis]]

Projects:LabelSpace

2008-04-24T17:10:02Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Label_space.png|thumb|Figure 1: The first three label space configurations: a unit interval for two labels, a triangle for three labels, and a tetrahedron for four labels (left to right). ]]
[[Image:Ls_original_aligned.png|thumb|Figure 2: Alignment of a set of 30 maps used in the study by Tsai et al. (2003). The original and aligned sets are superimposed for visualization.]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

File:Ls original aligned.png

2008-04-24T17:09:17Z

Malcolm: Alignment of a set of 30 maps used in the study by Tsai et al. (2003). The original and aligned sets are superimposed for visualization

Alignment of a set of 30 maps used in the study by Tsai et al. (2003). The original and aligned sets are superimposed for visualization

Projects:LabelSpace

2008-04-24T17:02:27Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Label_space.png|thumb|Figure 1: The first three label space configurations: a unit interval for two labels, a triangle for three labels, and a tetrahedron for four labels (left to right). ]]
[[Image:Table2.png|thumb|600px|Figure 2: Figure 1: Table gives the number of mislabelled voxels for each of the methods for left hippocampus]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

File:Label space.png

2008-04-24T17:01:57Z

Malcolm:

The first three label space configurations: a unit interval for two labels, a triangle for
three labels, and a tetrahedron for four labels (left to right).

File:Label space.png

2008-04-24T17:01:14Z

Malcolm:

<math>\triangle f</math>

The first three label space configurations: a unit interval
<math>\mathbb{L}^2 \subset \mathbb{R}</math> for two labels, a triangle $\L[3] \subset \R^2$ for
three labels, and a tetrahedron $\L[4] \subset \R^3$ for four labels
(left to right)

Projects:LabelSpace

2008-04-24T16:54:12Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Label_space.png|thumb|Figure 1: First four configurations of label space]]
[[Image:Table2.png|thumb|600px|Figure 2: Figure 1: Table gives the number of mislabelled voxels for each of the methods for left hippocampus]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

Projects:LabelSpace

2008-04-24T16:53:52Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Label_space.png|thumb|600px|Figure 1: First four configurations of label space]]
[[Image:Table2.png|thumb|600px|Figure 2: Figure 1: Table gives the number of mislabelled voxels for each of the methods for left hippocampus]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

File:Label space.png

2008-04-24T16:53:21Z

Malcolm: First four label space configurations.

First four label space configurations.

Projects:LabelSpace

2008-04-24T16:51:27Z

Malcolm:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

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.

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Table1.png|thumb|600px|Figure 1: Table gives the number of mislabelled voxels for each of the methods for left caudate nucleus]]
[[Image:Table2.png|thumb|600px|Figure 2: Figure 1: Table gives the number of mislabelled voxels for each of the methods for left hippocampus]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

[[Category: Shape Analysis]]

Projects:LabelSpace

2008-04-24T16:38:03Z

Malcolm: New page: Back to Georgia Tech Algorithms __NOTOC__ = Label Space: A Coupled Multi-Shape Representation = Our Objective is to compare various shape representation techniques l...

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Label Space: A Coupled Multi-Shape Representation =

Our Objective is to compare various shape representation techniques like linear PCA (LPCA), kernel PCA (KPCA), locally linear embedding (LLE) and
kernel locally linear embedding (KLLE).

= Description =

The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].

[[Image:Table1.png|thumb|600px|Figure 1: Table gives the number of mislabelled voxels for each of the methods for left caudate nucleus]]
[[Image:Table2.png|thumb|600px|Figure 2: Figure 1: Table gives the number of mislabelled voxels for each of the methods for left hippocampus]]

= Key Investigators =

* Georgia Tech Algorithms: James Malcolm, Yogesh Rathi, Allen Tannenbaum

= Publications =

''In Print''
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3AKPCA+LLE+KLLE+ShapeAnalysis&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]

[[Category: Shape Analysis]]

Algorithm:GATech

2008-04-24T16:34:36Z

Malcolm:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms =

At Georgia Tech, 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.

= Georgia Tech Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Gatech caudateBands.PNG|200px]]
| style="width:85%" |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

|-

| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> 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.

|-

| | [[Image:Results brain sag.JPG|200px|]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly 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...]]

<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

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...]]

<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

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...]]

<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

<font color="red">'''New: '''</font> Currently under investigation.

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

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...]]

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|}

File:P1 small.png

2008-04-24T16:34:12Z

Malcolm: Manually segmented amygdala, hippocampus, parahippocampus.

Manually segmented amygdala, hippocampus, parahippocampus.

Algorithm:GATech

2008-04-24T16:24:05Z

Malcolm: /* Label Space: A Coupled Multi-Shape Representation */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms =

At Georgia Tech, 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.

= Georgia Tech Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Gatech caudateBands.PNG|200px]]
| style="width:85%" |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

|-

| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> 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.

|-

| | [[Image:Results brain sag.JPG|200px|]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly 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...]]

<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

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...]]

<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

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...]]

<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

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| | [[Image:Stochastic-snake.png|200px|]]
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

<font color="red">'''New: '''</font> Currently under investigation.

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| | [[Image:Table1.png|200px|]]
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.

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| | [[Image:Gatech SlicerModel2.jpg|200px]]
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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| | [[Image:TruckInitialization.png|200px|]]
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

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...]]

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|}

Algorithm:GATech

2008-04-24T16:22:20Z

Malcolm: added Label Space project

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms =

At Georgia Tech, 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.

= Georgia Tech Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Gatech caudateBands.PNG|200px]]
| style="width:85%" |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

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| | [[Image:ZoomedResultWithModel.png|200px]]
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> 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.

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| | [[Image:Results brain sag.JPG|200px|]]
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly 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...]]

<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

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| | [[Image:TruckInitialization.png|200px|]]
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

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| | [[Image:Dlpfc1.jpg|200px|]]
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.

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| | [[Image:Striatum1.png|200px|]]
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

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| | [[Image:Brain-flat.PNG|200px]]
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== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

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...]]

<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.

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| | [[Image:Basis membership.png|200px]]
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

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| | [[Image:Circle seg.PNG|200px|]]
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

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...]]

<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.

|-

| | [[Image:Fig1yan.PNG|200px|]]
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

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...]]

<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

<font color="red">'''New: '''</font> Currently under investigation.

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

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...]]

<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
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...]]

J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Multi-Object Shape Representation." Combinatorial Image Analysis, 2008.

|}