NAMIC Wiki - User contributions [en]

Mahnaz Maddah

2010-05-10T19:01:55Z

Mmaddah:

email: mmaddah at alum.mit.edu 

[http://people.csail.mit.edu/mmaddah Mahnaz's homepage]

Mahnaz Maddah

2010-02-11T16:39:06Z

Mmaddah:

email: maddah at ge dot edu 

[http://people.csail.mit.edu/mmaddah Mahnaz's homepage]

File:Shot2.png

2010-01-07T16:57:40Z

Mmaddah:

File:Shot1.png

2010-01-07T16:56:29Z

Mmaddah:

AHM 2010 Tutorial Contest - EM Fiber Clustering

2010-01-07T16:49:28Z

Mmaddah:

EM Fiber Clustering Module

{|
|[[File:shot1.png|thumb|280px|Module interface]]
|[[File:shot2.png|thumb|280px|Trajectories colored by their cluster ID]]
|}

== General Information ==

===Authors, Collaborators & Contact===
* Mahnaz Maddah
* James Miller
* Contact: Mahnaz Maddah, maddah@ge.com

===Module Description===

This module clusters a set of input trajectories into a number of bundles, generates arc length parameterization by establishing the point correspondences and reports diffusion parameters along the bundles as well as the membership probability of each trajectory in each cluster. The module requires specification of seed trajectories (or initial centerlines) as representatives of the desired bundles.

== Usage ==

===Examples, Use Cases & Tutorials===

[http://www.na-mic.org/Wiki/index.php/File:FiberClusteringTrainingTutorial_Winter2010AHM.pdf Fiber Clustering Tutorial Slides]

Test data can be downloaded from here:

http://www.nitrc.org/projects/quantitativedti/

===Quick Tour of Features and Use===
Here is a list of the panels in the module:

* '''IO panel:'''
''Trajectories'' Input trajectories to be clustered.
''Output Clusters'' Clustered trajecories labeled by their cluster ID.
''Initial Centers'' [optional] Initial center(s). Note that intial centers need
to be provided by passing either a set of trajectories here or a fiducial list.
''Fiducials to Pick Initial Centers'' [optional] A fiducial list to generate initial center(s). For each fiducial
in the list the closest trajectory in the input is selected as the initial center.
''Output Initial Centers'' [Optional] Selected initial cluster centers.
''Output Final Centers'' [Optional] Final cluster centers, colored by the mean FA value along the bundle
if "Perform Quantitative Analysis" is flaged .
''Perform Quantitative Analysis'' Flag that needs to be marked if quantitative analysis is desired to be done.
''Output Directory'' A directory needs to be specified if performing quantitative analysis.
''File Prefix Name'' Prefix of the output files generated through tract-oriented analysis.
''[[CSVfilesDescription|Description of generated CSV files by EM Clustering Module]]''

* '''Clustering Parameters:'''
''Compactness of Fiber Bundles'' Parameter between 1 and 5 that specifies the extent of similarity between the
trajectories of each cluster. Increase the value for more compact bundles.

* '''Advanced Parameters:'''
''Space Resolution'' Space resolution for distance map calculation.
''Iterations'' Maximum number of EM iterations.
''Maximum Distance'' Maximum distance in mm specifies an upper threshold on the distance of points that
can contribute to new center formation.

===References===

[http://www.slicer.org/publications/pages/display?search=mahnaz+maddah&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked Publications]

AHM 2010 Tutorial Contest - EM Fiber Clustering

2010-01-07T16:42:16Z

Mmaddah: /* Downloads */

==Key Investigators==
* Mahnaz Maddah, Jim Miller (GE Research)

==Downloads==
[http://www.na-mic.org/Wiki/index.php/File:FiberClusteringTrainingTutorial_Winter2010AHM.pdf Fiber Clustering Tutorial Slides]

AHM 2010 Tutorial Contest - EM Fiber Clustering

2010-01-06T15:26:35Z

Mmaddah:

=== Downloads ===

[http://www.na-mic.org/Wiki/index.php/File:FiberClusteringTrainingTutorial_Winter2010AHM.pdf Fiber Clustering Tutorial Slides]

File:FiberClusteringTrainingTutorial Winter2010AHM.pdf

2010-01-06T15:22:24Z

Mmaddah:

AHM 2010 Tutorial Contest - EM Fiber Clustering

2010-01-06T15:15:48Z

Mmaddah: Created page with '=== Downloads === # [FiberClusteringTrainingTutorial_Winter2010AHM.pdf Tutorial Slides]'

=== Downloads ===

# [FiberClusteringTrainingTutorial_Winter2010AHM.pdf Tutorial Slides]

AHM2010:Tutorial Contest

2010-01-06T15:08:57Z

Mmaddah: /* Tutorial List */

=Background=

[http://www.slicer.org Slicer3] is now being used to perform meaningful research tasks. As part of the NA-MIC Training Core activities we are building a curated portfolio of tutorials for the basic functions and functionality available in Slicer. For examples for such existing tutorials as well as tutorials of the past contests are posted on the [http://wiki.na-mic.org/Wiki/index.php/Slicer3:Training#Training_Compendium|NA-MIC training compendium].

=Tutorial Contest Goal=
The primary purpose of this contest is to enrich the training materials that are available to end-users and developers using 3D Slicer and the NA-MIC kit. We believe contestants will be motivated to participate to enhance the dissemination of their own algorithms that they have incorporated into the Slicer3 platform and/or to enhance training of Slicer3 functionality for their own laboratory groups.

There will be two categories:
#'''END TO END SOLUTION TUTORIAL:''' In this category, the tutorial will teach a user how to solve a particular clinical problem using the NA-MIC Kit. Entries into this category will require at least:
#*materials about the scientific and application background and motivation,
#*step-by-step guides, and
#*sample data
#*Example: [[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ ARTIC (Automatic Regional Cortical Thickness) Tutorial]]
#'''ALGORITHM TUTORIAL:''' In this category the tutorial will teach a user how to make an algorithm work on their data. Entries into this category will require at least:
#*materials about the scientific and application background of the algorithm(s) and their use in the Slicer environment
#*step-by-step guides, and
#*at least two different sample data sets from two different institutions
#*Example: [[media:EMSegment_TrainingTutorial.pdf| Non-human Primates Segmentation Tutorial]]

=Template=
A basic template has been used for all of the tutorials. The same design should be used for the contest. It can be found here: [[Media:TrainingTutorialTemplate.ppt|Template]]
*Note: The examples above predate the template.

=Rules=
*Tutorial must be based on a snapshot or release of Slicer 3
*Tutorial must follow the guidelines specified above
*If applicable, provide clear directions for downloading and installing additional modules
*The tutorial and all of its components (data, powerpoints/pdfs, additional modules etc.) must be released under the [http://www.slicer.org/slicerWiki/index.php/Slicer:license Slicer license]
*Applicants must agree to work with the NA-MIC Training and Dissemination Cores to curate their submission (we will test it on each of the available platforms and for usability and work with you to smooth any issues after the contest).

=Dates and Submission Dead-line=

*Presentation: all tutorials will be presented by the authors on '''Wednesday January 6 from 8 am to 9 am''' during the Project Week. Each tutorial presentation should be 10 minutes long.
* If you wish to participate in the contest, please create a wiki page for your tutorial, upload your slides and tutorial dataset and add a link to your tutorial page in the section below. Please name your tutorial file as 'TutorialName_Winter2010AHM.pdf' and tutorial data as 'TutorialName_Data_Winter2010AHM.pdf'
'''Submission dead-line: Monday January 4, 2010'''

=Tutorial List=
#[[AHM 2010 Tutorial Contest - Hammer Registration | Hammer Registration ]] (Guorong Wu)
#[[AHM 2010 Tutorial Contest - CoronaryArteriesCenterlinesVMTK | Centerline Extraction of Coronary Arteries using VMTK]] (Daniel Haehn)
#[[AHM 2010 Tutorial Contest - Port of Slicer to Qt| Port of Slicer to Qt]] (Julien Finet)
#[[AHM 2010 Tutorial Contest - EM Fiber Clustering| EM Fiber Clustering]] (Mahnaz Maddah)

Projects:DTIModeling

2007-04-25T14:52:25Z

Mmaddah: /* Discription */

= Discription =

Collaborators: Mahnaz Maddah, Sandy Wells, Simon Warfield and Eric Grimson.

----

We developed a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis.

We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster and an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center.

The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. Here are some examples of modeling/clustering the bundles:

[[Image:models.jpg|Model of fiber tracts]]

[[Image:wholebrain.jpg|Model of fiber tracts]]

One of the difficult bundles of fiber tracts to cluster is the cingulum. Even starting tractography from a user-defined ROI results in a set of disordered trajectories, mostly short in length because of low FA. Also, due to its adjacency to the corpus callosum, many callosal trajectories are included that adversely affect any further analysis of the bundle. As shown in the following figure for two subjects, our method is well capable of clustering these trajectories into the desired bundles. Two arbitrary trajectories, one from the the superior and one from the posterior part of the cingulum were selected as the initial cluster centers. Knowledge of the point correspondence and hence rigorous calculation of the similarity measure is essential for clustering of such a disordered set of trajectories.

[[Image:cingulum.jpg]]

Figure below illustrates the evolution of the Gamma distribution for the clusters of the first case shown the above figure. Convergence is achieved just after a few iterations of the EM algorithm.

[[Image:Gamma.jpg|300px]]

An example of tract-based quantitative analysis is shown below for five bundles of fiber tracts. The FA is plotted vs. the arc length. Note that since the point correspondance between the trajectories is already known with our clustering algorithm, no further aligining is needed for performing quantitative analysis along the tracts.

[[Image:5bundles.jpg|200px]]
[[Image:FAs.jpg]]

= Publications =

[1] M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

[2] M. Maddah, S. K. Warfield, W. E. L. Grimson, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts, Accepted for publication in Medical Image Analysis.

[3] M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson,
A Spatial Model of White Matter Fiber Tracts
to be presented at ISMRM 2007, Berlin.

[4] M. Maddah, W. E. L. Grimson, and S. Warfield,
Statistical Modeling and EM Clustering of White Matter Fiber Tracts
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI) 2006, pp. 53-56.

[5] D. Goldberg-Zimring, A. U. J. Mewes, M. Maddah, S. K. Warfield,
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis
J Neuroimaging, vol. 15, pp. 68S-81S, 2005.

[6] M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield,
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.
MICCAI05, Palm Spring, CA, pp. 188-195, 2005.

= Software =

Currently, all of the codes are implemented in MATLAB.

Projects:DTIModeling

2007-04-25T14:35:01Z

Mmaddah: /* Discription */

= Discription =

Collaborators: Mahnaz Maddah, Sandy Wells, Simon Warfield and Eric Grimson.

----

We developed a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis.

We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster and an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center.

The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. Here are some examples of modeling/clustering the bundles:

[[Image:models.jpg|Model of fiber tracts]]

[[Image:wholebrain.jpg|Model of fiber tracts]]

One of the difficult bundles of fiber tracts to cluster is the cingulum. Even starting tractography from a user-defined ROI results in a set of disordered trajectories, mostly short in length because of low FA. Also, due to its adjacency to the corpus callosum, many callosal trajectories are included that adversely affect any further analysis of the bundle. As shown in the following figure for two subjects, our method is well capable of clustering these trajectories into the desired bundles. Two arbitrary trajectories, one from the the superior and one from the posterior part of the cingulum were selected as the initial cluster centers. Knowledge of the point correspondence and hence rigorous calculation of the similarity measure is essential for clustering of such a disordered set of trajectories.

[[Image:cingulum.jpg]]

Figure below illustrates the evolution of the Gamma distribution for the clusters of the first case shown the above figure. Convergence is achieved just after a few iterations of the EM algorithm.

[[Image:gamma.jpg|300px]]

An example of tract-based quantitative analysis is shown below for five bundles of fiber tracts. The FA is plotted vs. the arc length. Note that since the point correspondance between the trajectories is already known with our clustering algorithm, no further aligining is needed for performing quantitative analysis along the tracts.

[[Image:5bundles.jpg|200px]]
[[Image:FAs.jpg]]

= Publications =

[1] M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

[2] M. Maddah, S. K. Warfield, W. E. L. Grimson, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts, Accepted for publication in Medical Image Analysis.

[3] M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson,
A Spatial Model of White Matter Fiber Tracts
to be presented at ISMRM 2007, Berlin.

[4] M. Maddah, W. E. L. Grimson, and S. Warfield,
Statistical Modeling and EM Clustering of White Matter Fiber Tracts
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI) 2006, pp. 53-56.

[5] D. Goldberg-Zimring, A. U. J. Mewes, M. Maddah, S. K. Warfield,
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis
J Neuroimaging, vol. 15, pp. 68S-81S, 2005.

[6] M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield,
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.
MICCAI05, Palm Spring, CA, pp. 188-195, 2005.

= Software =

Currently, all of the codes are implemented in MATLAB.

File:FAs.jpg

2007-04-25T14:33:08Z

Mmaddah:

File:5bundles.jpg

2007-04-25T14:27:27Z

Mmaddah:

Projects:DTIModeling

2007-04-25T14:25:47Z

Mmaddah: /* Discription */

= Discription =

Colaborators: Mahnaz Maddah, Sandy Wells, Simon Warfield and Eric Grimson.

We developed a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis.

We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster and an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center.

The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. Here are some examples of modeling/clustering the bundles:

[[Image:models.jpg|Model of fiber tracts]]

[[Image:wholebrain.jpg|Model of fiber tracts]]

One of the difficult bundles of fiber tracts to cluster is the cingulum. Even starting tractography from a user-defined ROI results in a set of disordered trajectories, mostly short in length because of low FA. Also, due to its adjacency to the corpus callosum, many callosal trajectories are included that adversely affect any further analysis of the bundle. As shown in the following figure for two subjects, our method is well capable of clustering these trajectories into the desired bundles. Two arbitrary trajectories, one from the the superior and one from the posterior part of the cingulum were selected as the initial cluster centers. Knowledge of the point correspondence and hence rigorous calculation of the similarity measure is essential for clustering of such a disordered set of trajectories.

[[Image:cingulum.jpg]]

Figure below illustrates the evolution of the Gamma distribution for the clusters of the first case shown the above figure. Convergence is achieved just after a few iterations of the EM algorithm.

[[Image:gamma.jpg|300px]]

An example of tract-based quantitative analysis is shown below for five bundles of fiber tracts. The FA is plotted vs. the arc length. Note that since the point correspondance between the trajectories is already known with our clustering algorithm, no further aligining is needed for performing quantitative analysis along the tracts.

[[Image:5bundles.jpg]]
[[Image:FAs.jpg]]

= Publications =

[1] M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

[2] M. Maddah, S. K. Warfield, W. E. L. Grimson, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts, Accepted for publication in Medical Image Analysis.

[3] M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson,
A Spatial Model of White Matter Fiber Tracts
to be presented at ISMRM 2007, Berlin.

[4] M. Maddah, W. E. L. Grimson, and S. Warfield,
Statistical Modeling and EM Clustering of White Matter Fiber Tracts
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI) 2006, pp. 53-56.

[5] D. Goldberg-Zimring, A. U. J. Mewes, M. Maddah, S. K. Warfield,
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis
J Neuroimaging, vol. 15, pp. 68S-81S, 2005.

[6] M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield,
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.
MICCAI05, Palm Spring, CA, pp. 188-195, 2005.

= Software =

Currently, all of the codes are implemented in MATLAB.

Projects:DTIModeling

2007-04-25T14:17:54Z

Mmaddah: /* Discription */

= Discription =

We present a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis.

We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster and an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center.

The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. Here are some examples of modeling/clustering the bundles:

[[Image:models.jpg|Model of fiber tracts]]

[[Image:wholebrain.jpg|Model of fiber tracts]]

One of the difficult bundles of fiber tracts to cluster is the cingulum. Even starting tractography from a user-defined ROI results in a set of disordered trajectories, mostly short in length because of low FA. Also, due to its adjacency to the corpus callosum, many callosal trajectories are included that adversely affect any further analysis of the bundle. As shown in the following figure for two subjects, our method is well capable of clustering these trajectories into the desired bundles. Two arbitrary trajectories, one from the the superior and one from the posterior part of the cingulum were selected as the initial cluster centers. Knowledge of the point correspondence and hence rigorous calculation of the similarity measure is essential for clustering of such a disordered set of trajectories.

[[Image:cingulum.jpg]]

Figure below illustrates the evolution of the Gamma distribution for the clusters of the first case shown the above figure. Convergence is achieved just after a few iterations of the EM algorithm.

[[Image:gamma.jpg]]

An example of tract-based quantitative analysis is shown below for five bundles of fiber tracts. The FA is plotted vs. the arc length. Note that since the point correspondance between the trajectories is already known with our clustering algorithm, no further aligining is needed for performing quantitative analysis along the tracts.

[[Image:5bundles.jpg]]
[[Image:FAs.jpg]]

= Publications =

[1] M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

[2] M. Maddah, S. K. Warfield, W. E. L. Grimson, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts, Accepted for publication in Medical Image Analysis.

[3] M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson,
A Spatial Model of White Matter Fiber Tracts
to be presented at ISMRM 2007, Berlin.

[4] M. Maddah, W. E. L. Grimson, and S. Warfield,
Statistical Modeling and EM Clustering of White Matter Fiber Tracts
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI) 2006, pp. 53-56.

[5] D. Goldberg-Zimring, A. U. J. Mewes, M. Maddah, S. K. Warfield,
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis
J Neuroimaging, vol. 15, pp. 68S-81S, 2005.

[6] M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield,
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.
MICCAI05, Palm Spring, CA, pp. 188-195, 2005.

= Software =

Currently, all of the codes are implemented in MATLAB.

File:Gamma.jpg

2007-04-25T14:15:30Z

Mmaddah:

File:Cingulum.jpg

2007-04-25T14:13:54Z

Mmaddah: clustering of cingulum

clustering of cingulum

File:Wholebrain.jpg

2007-04-25T14:12:03Z

Mmaddah: whole brain clustering

whole brain clustering

Projects:DTIModeling

2007-04-23T22:17:18Z

Mmaddah: /* Publications */

Projects:DTIModeling

2007-04-23T22:14:14Z

Mmaddah: /* Publications */

Algorithm:MIT

2007-04-23T21:04:35Z

Mmaddah: /* DTI Analysis and Visualization */

= Shape- and Atlas-Based Segmentation =

The goal of this project is to augment the segmentation process with prior information on the shape of the anatomical structures (shape atlas) learned from previously segmented scans (using, for example, Principal Component Analysis). We are working on methods that integrate the shape atlases with segmentation algorithms.

=== Tissue Classification ===

This type of algorithms assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|More...]]

'''New: ''' K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Accapted to the Special Issue of Best Selected Papers from MICCAI 06 in Medical Image Analysis [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Publications|More...]]

'''New: ''' K.M. Pohl, R. Kikinis, and W.M. Wells. Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework. Accapted to IPMI 2007. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Publications|More...]]

[[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|'''Description''']] - [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Publications|'''Publications''']] - [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Software|'''Software''']] -
[[AHM_2006:ProjectsJointRegistrationSegmentation|''' AHM 2006''']] -
[[AHM_2007:Slicer3_Developer_Feedback#EM|''' AHM 2007''']]

=== Boundary Localization ===

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|More...]]

[[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|'''Description''']] - [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation#Publications|'''Publications''']] - [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation#Software|'''Software''']]

=== Registration Regularization ===

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation.
[[Algorithm:MIT:RegistrationRegularization|More...]]

'''New:''' Submission for MICCAI 2007

= DTI Analysis and Visualization =

Our work in DTI analysis focuses on identifying new ways to provide an interpretation of the white matter connectivity and to utilize the information contained in the DTI images to create more comperehsive models of the brain architecture.

=== DTI Fiber Clustering/Atlas Creation/Analysis ===

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Algorithm:MIT:DTI_Clustering|More...]]

'''New:'''
Monica E. Lemmond, Lauren J. O'Donnell, Stephen Whalen, Alexandra J. Golby.
Characterizing Diffusion Along White Matter Tracts Affected by Primary Brain Tumors.
Accepted to HBM 2007.
[[Algorithm:MIT:DTI_Clustering#Publications|More...]]

[[Algorithm:MIT:DTI_Clustering|'''Description''']] -
[[Algorithm:MIT:DTI_Clustering#Publications|'''Publications''']] -
[[Algorithm:MIT:DTI_Clustering#Software|'''Software''']] -
[[AHM_2006:ProjectsWhiteMatterClustering|'''AHM 2006''']] - [[NA-MIC/Projects/Diffusion_Image_Analysis/Slicer_Fiber_Anatomical_Labeling|'''PW 2006''']]

=== Fiber Tract Modeling, Clustering and Quantitative Analysis ===

The goal of this work is to model the shape of the fiber bundles and use this model discription in clustering and statistical analysis of fiber tracts. [[Algorithm:MIT:DTI_Modeling|More...]]

'''New:'''
M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson, Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

'''New:'''
M. Maddah, S. K. Warfield, W. E. L. Grimson, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts, Accepted for publication in Medical Image Analysis.

'''New:'''
M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson, A Spatial Model of White Matter Fiber Tracts to be presented at ISMRM 2007, Berlin.

[[Algorithm:MIT:DTI_Modeling|'''Description''']] - [[Algorithm:MIT:DTI_Modeling#Publications|'''Publications''']] - [[Algorithm:MIT:DTI_Modeling#Software|'''Software''']]

=== DTI-based Segmentation ===

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Algorithm:MIT:DTI_Segmentation|More...]]

'''New:''' Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006. [[Algorithm:MIT:DTI_Segmentation#Publications|More...]]

[[Algorithm:MIT:DTI_Segmentation|'''Description''']] - [[Algorithm:MIT:DTI_Segmentation#Publications|'''Publications''']] - [[Algorithm:MIT:DTI_Segmentation#Software|'''Software''']]

=== Fiber-Tract-Bundle-based Non-Linear Registration ===

The goal of this work is to utilize the anatomical information from segmented fiber bundles and use this information for registering fiber tracts and the underlying DTI images. [[Algorithm:MIT:DTI_FiberRegistration|More...]]

[[Algorithm:MIT:DTI_FiberRegistration|'''Description''']] - [[Algorithm:MIT:DTI_FiberRegistration#Publications|'''Publications''']] - [[Algorithm:MIT:DTI_FiberRegistration#Software|'''Software''']]

= fMRI Detection and Analysis =

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Algorithm:MIT:fMRI_Detection|More...]]

'''New:''' Wanmei Ou, Sandy Wells, Polina Golland. Bridging Spatial Regularization And Anatomical Priors in fMRI Detection. In preparation for submission to IEEE TMI. [[Algorithm:MIT:fMRI_Detection#Publications|More...]]

[[Algorithm:MIT:fMRI_Detection|'''Description''']] - [[Algorithm:MIT:fMRI_Detection#Publications|'''Publications''']] - [[Algorithm:MIT:fMRI_Detection#Software|'''Software''']] - [[NA-MIC/Projects/fMRI_Analysis/Spatial_Regularization_for_fMRI_Detection|'''PW 2006''']]

= Population Analysis of Anatomical Variability=

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Algorithm:MIT:Shape_Analisys|More...]]

'''New:''' Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

[[Algorithm:MIT:Shape_Analisys|'''Description''']] - [[Algorithm:MIT:Shape_Analisys#Publications|'''Publications''']] - [[Algorithm:MIT:Shape_Analisys#Software|'''Software''']] - [[AHM_2006:ProjectsShapeAnalysis|'''AHM 2006''']]

= Groupwise Registration =

We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]

'''New:''' Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.

[[Algorithm:MIT:Groupwise_Registration#Introduction|'''Description''']] - [[Algorithm:MIT:Groupwise_Registration#Software|'''Software''']]

= Collaborations with other groups in NAMIC =

* Algorithms:
** Segmentation: joint development of the algorithms and GUI for shape-based hierarchical segmentation with BWH (Kilian Pohl, Steve Pieper).
** Shape Analysis: joint pipeline I/O formulation and development with Kitware (Brad Davis) and UNC (Martin Styner).
** fMRI Detection: joint integration of fMRI detectors into Slicer with BWH (Steve Pieper).

* Clinical:
** Continuing collaboration with [[DBP:Harvard|Harvard]] on shape-based segmentation and DTI analysis.
** New collaboration, enabled and facilitated by NAMIC, with [[DBP:Dartmouth|Dartmouth]] on DTI and fMRI analysis.

Projects:DTIModeling

2007-04-06T07:15:56Z

Mmaddah: /* Software */

= Publications =

[1] M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

[2] M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson,
A Spatial Model of White Matter Fiber Tracts
to be presented at ISMRM 2007, Berlin.

[3] M. Maddah, W. E. L. Grimson, and S. Warfield,
Statistical Modeling and EM Clustering of White Matter Fiber Tracts
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI) 2006, pp. 53-56.

[4] D. Goldberg-Zimring, A. U. J. Mewes, M. Maddah, S. K. Warfield,
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis
J Neuroimaging, vol. 15, pp. 68S-81S, 2005.

[5] M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield,
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.
MICCAI05, Palm Spring, CA, pp. 188-195, 2005.

= Software =

Currently, all of the codes are implemented in MATLAB.

Projects:DTIModeling

2007-04-06T07:15:06Z

Mmaddah: /* Publications */

Publications

2007-04-04T19:22:54Z

Mmaddah: /* Peer Reviewed Conference Proceedings */

The following publications gratefully acknowledge the support of NA-MIC. Please see [[Publications:NIH_Roadmap_acknowledgement|'''NA-MIC acknowledgement instructions''']] for instructions on how to acknowledge NA-MIC in your publications.

* '''Search Medline'''

# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=EB005149 Search Medline for NAMIC-enabled publications]

List of publications from other NCBCs:

# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=RR021813 Search Medline for CCB-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=lm008748 Search Medline for I2B2-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=gm072970 Search Medline for SIMBIOS-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=DA021519 Search Medline for NCIBI-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=CA121852 Search Medline for C2B2-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=HG004028 Search Medline for Bioontology-enabled publications]

 

* ** Note: Many technical journals are not listed in Medline. Medline therefore only holds a subset of NA-MIC related refereed publications.

== Peer Reviewed Journal Papers ==

# Nakamura M, McCarley RW, Kubicki M, Dickey CC, Niznikiewicz MA, Voglmaier MM, Seidman LJ, Maier SE, Westin CF, Kikinis R, Shenton ME. Fronto-temporal disconnectivity in schizotypal personality disorder: a diffusion tensor imaging study. Biol Psychiatry. 2005 Sep 15;58(6):468-78. [[Media:Nakamura_2005BiolPsych.pdf| PDF]]
# Tuch DS, Salat DH, Wisco JJ, Zaleta AK, Hevelone ND, Rosas HD. Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proc Natl Acad Sci U S A. 2005 Aug 23;102(34):12212-7. [[Media:Tuch_PNAS_2005.pdf| PDF]]
# Tuch DS, Wisco JJ, Khachaturian MH, Ekstrom LB, Kotter R, Vanduffel W. Q-ball imaging of macaque white matter architecture. Philos Trans R Soc Lond B Biol Sci. 2005 May 29;360(1457):869-79. [[Media:Tuch_PhilosTransRBiolSoc_2005.pdf| PDF]]
# Niethammer M, Vela P, Tannenbaum A. On the evolution of closed curves by means of vector distance functions. Int. Journal Computer Vision, 2006. [[Media:Niethammer_etal_VISI849-04I.pdf| PDF]]
# Niethammer M, Tannenbaum A, Angenent S. Dynamic active contours. IEEE Trans. Automatic Control. 2006; 51:562-579. [[Media:Niethammer_paper1.pdf| PDF]]
# Turner JA, Smyth P, Macciardi F, Fallon JH, Kennedy JL, Potkin SG. Imaging phenotypes and genotypes in schizophrenia. Neuroinformatics. 2006;4(1):21-49. [[Media:Media-Turner_paper.pdf| PDF]]
# C Cascio, M Styner, RG Smith, M Poe, G Gerig, H Hazlett, M Jomier, R Bammer, J Piven, Reduced relationship to cortical white matter revealed by tractography-based segmentation of the corpus callosum in yound children with developmental delay, Am J Psychiatry, 2006, (163) 2157-2163, December.

'''''In Press'''''
# Nain D, Haker S, Bobick A, Tannenbaum A. "Multiscale 3D Shape Representation and Segmentation using Spherical Wavelets", in press, IEEE Transactions on Medical Imaging Special issue on Computational Anatomy.
# Liu T, Young G, Huang L, Chen N-K, Wong S. “76-space Analysis of Grey Matter Diffusivity: Methods and Applications,” in press, NeuroImage.
# O’Donnell L, Kubicki M, Shenton ME, Dreusicke MH, Grimson WEL, Westin CF. A method for clustering white matter fiber tracts. AJNR (In Press).
# Koo MS, Levitt JJ, McCarley RW, Seidman LJ, Dickey CC, Niznikiewicz MA, Voglmaier MM, Zamani P, Long KL, Kim SS, Shenton ME. Reduction of caudate volume in neuroleptic-naive female subjects with schizotypal personality disorder. Biol Psychiatry (In Press).
# Kuroki N, Kubicki M, Nestor PG, Salisbury DF, Park HJ, Levitt JJ, Woolston S, Frumin M, Niznikiewicz M, Westin CF, Maier SE, McCarley RW, Shenton ME. Fornix integrity and hippocampal volume in male schizophrenic patients. Biol Psychiatry (In Press).
# Onitsuka T, Niznikiewicz MA, Spencer KM, Frumin M, Kuroki N, Lucia LC, Shenton ME, McCarley RW. Schizophrenia is associated with functional and structural deficits in brain regions subserving face processing. Am J Psychiatry (In Press).
# Niethammer M, Tannenbaum A, Kalies W, Mischaikow K. Detecting simple points in higher dimensions. IEEE Image Processing, 2006. (In Press).
# Rathi Y, Dambreville S, Tannenbaum A. Comparative analysis of kernel methods for statistical shape learning. CVAMIA'06, 2006. (In Press).
# Roth RM, Koven, NS, Randolph JJ, Flashman LA, Pixley HS, Ricketts SM, Wishart HA, Saykin AJ. Event-Related fMRI study of Functional magnetic resonance imaging of executive control in bipolar disorder. NeuroReport, 2006 (In Press).
# Pohl KM, Fisher J, Grimson WEL, Kikinis R, Wells WM. [[Media:Pohl-ni-2006.pdf| A bayesian model for joint segmentation and registration.]] NeuroImage, 2006 (In Press).
# Fletcher PT, Joshi S. Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data. Signal Processing, 2006. (In Press).

== Peer Reviewed Conference Proceedings ==

'''Conferences included here represent the major high quality conferences in medical image analysis (MICCAI, IPMI, MMBIA, ISBI). These conferences only accept submission of full papers and guarantee a peer review process by at least three reviewers and an area chair. Acceptance rates are below 40% for MICCAI, IPMI and MMBIA and around 50% for ISBI.'''

# M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson, Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts, IPMI 2007, Netherlands.
# Styner M, Xu SC, El-Sayed M, Gerig G, Correspondence Evaluation in Local Shape Analysis and Structural Subdivision, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
# Zhou C, Park DC, Styner M, Wang YM, ROI Constrained Statistical Surface Morphometry, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
# 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), April 12-15, 2007, Washington DC, USA.
# Kim S, Smyth P. Hierarchical Dirichlet processes with random effects. Advances in Neural Information Processing Systems 19, to appear, 2006. [[Media:Nips_hMDP_RE.pdf| PDF ]]
# Kim S, Smyth P, Stern H. A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data. To appear in MICCAI, Oct 2-5, 2006.[[Media:InfMM_miccai.pdf| PDF ]]
# Nain D, Haker S, Bobick A, Tannenbaum A. Shape-driven surface segmentation using spherical wavelets. MICCAI, LNCS 4190, Oct 2-5, 2006.
# Styner M, Jomier M, Gerig G: Closed and Open Source Neuroimage Analysis Tools and Libraries at UNC. IEEE Symposium on Biomedical Imaging ISBI. 2006; 702-705. [[Media:ISBI06_Neurolib.pdf| PDF ]]
# M. Maddah, W. E. L. Grimson, and S. Warfield, Statistical Modeling and EM Clustering of White Matter Fiber Tracts, ISBI 2006: IEEE 2006 International Symposium on Biomedical Imaging, Arlington, VA, April 6-9, 2006
# Gerig G, Joshi S, Fletcher T, Gorczowski K, Xu S, Pizer SM, Styner M. Statistics of populations of images and its embedded objects: Driving applications in neuroimaging. IEEE Symposium on Biomedical Imaging ISBI. 2006; 1120-1123.[[Media:ISBI06-GerigShape.pdf| PDF]]
# Pieper S, Lorensen W, Schroeder W, Kikinis R. [[Media:Pieper-ISBI-2006-revised.pdf| The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as An Open Platform for the Medical Image Computing Community.]] IEEE Symposium on Biomedical Imaging ISBI. 2006; 698-701.
# Pichon E, Westin CF, and Tannenbaum A. A Hamilton-Jacobi-Bellman approach to high angular diffusion tractography. Proceedings of MICCAI, 2005. [[Media:Pichon-miccai05.pdf| PDF]]
# Yang Y, Zhu L, Haker S, Tannenbaum A. On the harmonic skeleton and vessel data. Proceeedings of MICCAI, 2005. [[Media:YangMICCAI2005.pdf| PDF]]
# Styner M, Gimpel Smith R, Cascio C, Oguz I, Jomier M. Corpus Callosum Subdivision based on a Probabilistic Model of Inter-hemispheric Connectivity. Medical Image Computing and Computer Assisted Interventions MICCAI. 2005 LNCS 3750;765-772. [[Media:MICCAI05CCSubdiv.pdf| PDF]]
# Liu T, Young G, Huang L, Chen N-K, Wong S. “76-space Analysis of Grey Matter Diffusivity: Methods and Applications,” MICCAI 2005.[[Media:MICCAI-2005--461-Liu.pdf| PDF]]
# Pichon E, Tannenbaum A. Curve segmentation using directional information, relation to pattern detection. Proc IEEE International Conference on Image Processing (ICIP), 2005
# Nain D, Haker S, Bobick A, Tannenbaum A. [[Media:Nain.miccai2005.pdf| Multiscale 3D Shape Analysis using Spherical Wavelets]]. Proc MICCAI, LNCS 3750, Oct 26-29 2005; p 459-467.
# Corouge I, Fletcher PT, Joshi S, Gilmore JH, Gerig G. [[Media:Corouge-miccai-2005.pdf| Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis.]] Proc. MICCAI, Oct 26-29 2005; LNCS 3749, pp. 131-139
# Ou W, Golland P. From Spatial Regularization to Anatomical Priors in fMRI Analysis. Proc IPMI, Jul 10-15 2005; LNCS 3565: p 88-100. [[Media:OuGolland_IPMI2005.pdf| PDF]]
# Pohl KM, Fisher J, Levitt JJ, Shenton ME, Kikinis R, Grimson WEL, Wells WM. [[Media:Pohl-miccai-2005.pdf| A Unifying Approach to Registration, Segmentation, and Intensity Correction,]] In Proc. MICCAI 2005: Eigth International Conference on Medical Image Computing and Computer Assisted Intervention, Palm Springs, CA, USA, Springer-Verlag, Part I, vol. 3749 of Lecture Notes in Computer Science, pp. 310-318, 2005
# Pohl KM, Bouix S, Shenton ME, Grimson WEL, Kikinis R. [[Media:Pohl-miccai-short-2005.pdf| Automatic Segmentation Using Non-Rigid Registration,]] In short communications of MICCAI 2005: Eighth International Conference on Medical Image Computing and Computer Assisted Intervention, Palm Springs, CA, USA, 2005
# Martin-Fernandez M, Bouix S, Ungar L, McCarley RW, Shenton ME. Two Methods for Validating Brain Tissue Classifiers. MICCAI 2005, Palm Springs, CA, USA: Duncan J and Gerig G (Eds.): Lecture Notes in Computer Science, volume 3749, pp 515-522, 2005. Springer-Verlag Berlin Heidelberg, 2005. [[Media:Martin-fernandezMICCAI05.pdf| PDF]]
# Kim S, Smyth P, Stern H, Turner J. Parametric response surface models for analysis of multi-site fMRI data. In Proc. MICCAI 2005: Eighth International Conference on Medical Image Computing and Computer Assisted Intervention, Palm Springs, CA, USA, Springer-Verlag, Part I, vol. 3749 of Lecture Notes in Computer Science, pp. 352-359, 2005. [[Media:Seyoung-official.pdf|PDF]]
# O'Donnell L, Westin C-F. White Matter Tract Clustering and Correspondence in Populations. MICCAI 2005, Palm Springs, CA, USA: Duncan J, Gerig G (Eds.): Lecture Notes in Computer Science, volume 3749, pp 140-147, 2005. Springer-Verlag Berlin Heidelberg, 2005. [[Media:OdonnellMICCAI05.pdf| PDF]]
# M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield, Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI. MICCAI05, Palm Spring, CA,: Lecture Notes in Computer Science, volume 3749, pp. 188-195, 2005.

== Book Chapters ==

# Nain D, Tannenbaum A, Unal G, Yezzi A, Zeitouni O. On a stochastic model of geometric snakes. Mathematical Methods in Computer Vision: A Handbook, edited by Faugeras O, Paragios N, Springer-Verlag, 2005.
# Angenent S, Tannenbaum A, Yezzi A, Zeitouni O. Curve shortening and interacting particle systems. Book chapter in a volume edited by Hamid Krim, 2005.

== Others ==

=== Insight Journal ===

# Goodlett C, Corouge I, Jomier M, Gerig G. [http://hdl.handle.net/1926/39 A Quantitative DTI Fiber Tract Analysis Suite]. Insight Journal, 2005.
# Miller JV. [http://hdl.handle.net/1926/159 "Probability distributions for the Insight Toolkit."] Insight Journal, 2006.
# Gao Y, Melonakos J, Tannenbaum A. [http://hdl.handle.net/1926/225 "Conformal Flattening ITK Filter"] Insight Journal, 2006.
# Melonakos J, Krishnan K, Tannenbaum A. [[Media:Melonakos-ISC2006-bayesian.pdf| An ITK filter for Bayesian segmentation: itkBayesianClassifierImageFilter]]. Insight Journal, Jan 2006. http://hdl.handle.net/1926/160.
# Melonakos J, Al-Hakim R, Fallon J, Tannenbaum A. [[Media:Melonakos-ISC2005-bayesian.pdf| Knowledge-based segmentation of brain MRI scans using the Insight Toolkit]]. Insight Journal, Oct 2005. http://hdl.handle.net/1926/44.

=== Conferences and Workshops ===

# M. Styner, I. Oguz, S. Xu, D. Pantazis, and G. Gerig. Statistical group differences in anatomical shape analysis using hotelling T2 metric. Proc SPIE Medical Imaging Conference, in print, 2007.
# Rathi Y, Olver P, Sapiro G, Tannenbaum A. Affine Invariant Surface Evolutions for 3D Image Segmentation, SPIE 2006
# Rathi Y, Dambreville S, Tannenbaum A. Comparative Analyis of Kernel Methods for Statistical Shape Learning, 2nd Workshop on Computer Vision Approaches to Medical Image Analysis (in conjunction with ECCV) 2006.
# Eric Pichon, Delphine Nain, and Marc Niethammer. [[Media:Pichon-SPIEMI2006-laplace.pdf| A Laplace Equation Approach for Shape Comparison]]. Proc SPIE Medical Imaging, 2006.
# Al-Hakim R, Fallon J, Nain D, Melonakos J, Tannenbaum A. [[Media:Dlpfc-SPIE2006-full.pdf| A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter]]. Proc SPIE Medical Imaging, 2006.
# Pohl KM, Fisher J, Kikinis R, Grimson WEL, Wells WM. [[Media:Pohl-iccv-ws-2005.pdf| Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images,]] In Proc. ICCV 2005: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trend, An International Conference on Computer Vision Workshop, Beijing, China, Springer-Verlag, vol. 3765 of Lecture Notes in Computer Science, 2005
# Zöllei L, Learned-Miller E, Grimson WEL, Wells WM III. Efficient Population Registration of 3D Data. Proc ICCV 2005, Computer Vision for Biomedical Image Applications; Beijing, China
# Angenent S, Pichon E, Tannenbaum A. Mathematical methods in medical imaging. Bulletin of American Mathematical Association, 2006.
# O'Donnell L, Westin C-F. A High-Dimensional Fiber Tract Atlas. accepted to ISMRM 2006.
# Zöllei L, Wells WM III. Multi-modal Image Registration Using Dirichlet-encoded Prior Information. oral presentation at WBIR06
# Fletcher PT, Whitaker RT. Riemannian Metrics on the Space of Solid Shapes. accepted to MICCAI'06 Workshop on Mathematical Foundations of Computational Anatomy (MFCA).
# Cates J, Meyer M, Fletcher PT, Whitaker R. Entropy-Based Particle Systems for Shape Correspondence. accepted to MICCAI'06 Workshop on Mathematical Foundations of Computational Anatomy (MFCA).

=== Submitted and in Preparation ===

# Flashman LA, Roth RM, Pixley HS, Cleavinger HB, Saykin AJ, McAllister TW, Vidaver RM. (submitted). Cavum septum pellucidum in schizophrenia: Clinical and neuropsychological correlates.
# Szymczak A, Tannenbaum A, Stillman A, Mischaikow K. Vessel cores from 3D imagery: a topological approach. submitted to IEEE Trans. Medical Imaging, 2006.
# Michailovich O, Tanenbaum A. Fast approximation of smooth functions from samples of partial derivatives. Submitted for publication to IEEE Signal Processing, 2006.
# Zhu L, Yang Y, Haker S, and Tannenbaum A. An image morphing technique based on optimal mass preserving mapping. Submitted to IEEE Trans. Image Processing, 2006.
# Rathi Y, Vaswani N, Tannenbaum A, Yezzi Y. Tracking Deforming Objects using Particle Filtering for Geometric Active Contours. Submitted to IEEE PAMI, 2005.
# Rathi Y, Tannenbaum A. Kernel PCA for shape based segmentation of medical images. submitted to MICCAI, 2006.
# Gorczowski K, Gerig G, Fletcher T, Pizer SM, Styner M. Statistics of Pose and Shape in Multi-Object Complexes using Principal Geodesic Analysis. submitted to MICCAI, 2006.
# Goodlett C, Davis B, Jean R, Gilmore J, Gerig G. Improved Correspondence for DTI Population Studies via Unbiased Atlas Building. submitted to MICCAI, 2006.
# Corouge I, Fletcher PT, Sarang J, Gouttard S, Gerig G. Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis. submitted MedIA Journal, Jan. 2006
# Gribbin M, Clement M, Muller K, Poe M, Cascio C, Jomier M, Piven J, Gerig G. Statistical analysis of diffusion tensor image data based on first principles, submitted MICCAI'06, March 2006
# Flashman LA, Roth RM, Koven NS, McAllister TW, Vidaver RM, Pendergrass JC. Neural Correlates of Self-Evaluation in Individuals with Schizophrenia. in preparation.
# Basu S, Fletcher PT, Whitaker RT. Rician Noise Removal in Diffusion Tensor MRI, to appear MICCAI'06.
# Han X, Fischl B. Intensity Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms, submitted to IEEE Transactions on Medical Imaging.
# Yu P, Grant P, Qi Y, Han X, Ségonne F, Pienaar R, Busa E, Pacheco J, Makris N, Buckner R, Golland P, Fischl B. Cortical Surface Shape Analysis Based on Spherical Wavelets, submitted to IEEE Transactions on Medical Imaging
# Ségonne F, Pacheco J, Fischl B. Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops, submitted to IEEE Transactions on Medical Imaging
# Voineskos AN, Zai G, Bulgin N, Shaikh S, Lang D, Honer WG, Kennedy JL. MAG but not CNP Gene Associated with Total Brain White Matter. In Preparation. Presented in Part at World Congress of Psychiatric Genetics Annual Meeting. October, 2006.
# Voineskos AN, Bulgin N, Van Adrichem Q, Wong ACH, Lang D, Honer WG, Kennedy JL. MAG but not CNP Gene Associated with Schizophrenia. In Preparation.

Publications

2007-04-04T19:20:25Z

Mmaddah: /* Peer Reviewed Conference Proceedings */

The following publications gratefully acknowledge the support of NA-MIC. Please see [[Publications:NIH_Roadmap_acknowledgement|'''NA-MIC acknowledgement instructions''']] for instructions on how to acknowledge NA-MIC in your publications.

* '''Search Medline'''

# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=EB005149 Search Medline for NAMIC-enabled publications]

List of publications from other NCBCs:

# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=RR021813 Search Medline for CCB-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=lm008748 Search Medline for I2B2-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=gm072970 Search Medline for SIMBIOS-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=DA021519 Search Medline for NCIBI-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=CA121852 Search Medline for C2B2-enabled publications]
# [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed&term=HG004028 Search Medline for Bioontology-enabled publications]

 

* ** Note: Many technical journals are not listed in Medline. Medline therefore only holds a subset of NA-MIC related refereed publications.

== Peer Reviewed Journal Papers ==

# Nakamura M, McCarley RW, Kubicki M, Dickey CC, Niznikiewicz MA, Voglmaier MM, Seidman LJ, Maier SE, Westin CF, Kikinis R, Shenton ME. Fronto-temporal disconnectivity in schizotypal personality disorder: a diffusion tensor imaging study. Biol Psychiatry. 2005 Sep 15;58(6):468-78. [[Media:Nakamura_2005BiolPsych.pdf| PDF]]
# Tuch DS, Salat DH, Wisco JJ, Zaleta AK, Hevelone ND, Rosas HD. Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proc Natl Acad Sci U S A. 2005 Aug 23;102(34):12212-7. [[Media:Tuch_PNAS_2005.pdf| PDF]]
# Tuch DS, Wisco JJ, Khachaturian MH, Ekstrom LB, Kotter R, Vanduffel W. Q-ball imaging of macaque white matter architecture. Philos Trans R Soc Lond B Biol Sci. 2005 May 29;360(1457):869-79. [[Media:Tuch_PhilosTransRBiolSoc_2005.pdf| PDF]]
# Niethammer M, Vela P, Tannenbaum A. On the evolution of closed curves by means of vector distance functions. Int. Journal Computer Vision, 2006. [[Media:Niethammer_etal_VISI849-04I.pdf| PDF]]
# Niethammer M, Tannenbaum A, Angenent S. Dynamic active contours. IEEE Trans. Automatic Control. 2006; 51:562-579. [[Media:Niethammer_paper1.pdf| PDF]]
# Turner JA, Smyth P, Macciardi F, Fallon JH, Kennedy JL, Potkin SG. Imaging phenotypes and genotypes in schizophrenia. Neuroinformatics. 2006;4(1):21-49. [[Media:Media-Turner_paper.pdf| PDF]]
# C Cascio, M Styner, RG Smith, M Poe, G Gerig, H Hazlett, M Jomier, R Bammer, J Piven, Reduced relationship to cortical white matter revealed by tractography-based segmentation of the corpus callosum in yound children with developmental delay, Am J Psychiatry, 2006, (163) 2157-2163, December.

'''''In Press'''''
# Nain D, Haker S, Bobick A, Tannenbaum A. "Multiscale 3D Shape Representation and Segmentation using Spherical Wavelets", in press, IEEE Transactions on Medical Imaging Special issue on Computational Anatomy.
# Liu T, Young G, Huang L, Chen N-K, Wong S. “76-space Analysis of Grey Matter Diffusivity: Methods and Applications,” in press, NeuroImage.
# O’Donnell L, Kubicki M, Shenton ME, Dreusicke MH, Grimson WEL, Westin CF. A method for clustering white matter fiber tracts. AJNR (In Press).
# Koo MS, Levitt JJ, McCarley RW, Seidman LJ, Dickey CC, Niznikiewicz MA, Voglmaier MM, Zamani P, Long KL, Kim SS, Shenton ME. Reduction of caudate volume in neuroleptic-naive female subjects with schizotypal personality disorder. Biol Psychiatry (In Press).
# Kuroki N, Kubicki M, Nestor PG, Salisbury DF, Park HJ, Levitt JJ, Woolston S, Frumin M, Niznikiewicz M, Westin CF, Maier SE, McCarley RW, Shenton ME. Fornix integrity and hippocampal volume in male schizophrenic patients. Biol Psychiatry (In Press).
# Onitsuka T, Niznikiewicz MA, Spencer KM, Frumin M, Kuroki N, Lucia LC, Shenton ME, McCarley RW. Schizophrenia is associated with functional and structural deficits in brain regions subserving face processing. Am J Psychiatry (In Press).
# Niethammer M, Tannenbaum A, Kalies W, Mischaikow K. Detecting simple points in higher dimensions. IEEE Image Processing, 2006. (In Press).
# Rathi Y, Dambreville S, Tannenbaum A. Comparative analysis of kernel methods for statistical shape learning. CVAMIA'06, 2006. (In Press).
# Roth RM, Koven, NS, Randolph JJ, Flashman LA, Pixley HS, Ricketts SM, Wishart HA, Saykin AJ. Event-Related fMRI study of Functional magnetic resonance imaging of executive control in bipolar disorder. NeuroReport, 2006 (In Press).
# Pohl KM, Fisher J, Grimson WEL, Kikinis R, Wells WM. [[Media:Pohl-ni-2006.pdf| A bayesian model for joint segmentation and registration.]] NeuroImage, 2006 (In Press).
# Fletcher PT, Joshi S. Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data. Signal Processing, 2006. (In Press).

== Peer Reviewed Conference Proceedings ==

'''Conferences included here represent the major high quality conferences in medical image analysis (MICCAI, IPMI, MMBIA, ISBI). These conferences only accept submission of full papers and guarantee a peer review process by at least three reviewers and an area chair. Acceptance rates are below 40% for MICCAI, IPMI and MMBIA and around 50% for ISBI.'''

# M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts, IPMI 2007, Netherlands.
# Styner M, Xu SC, El-Sayed M, Gerig G, Correspondence Evaluation in Local Shape Analysis and Structural Subdivision, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
# Zhou C, Park DC, Styner M, Wang YM, ROI Constrained Statistical Surface Morphometry, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
# 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), April 12-15, 2007, Washington DC, USA.
# Kim S, Smyth P. Hierarchical Dirichlet processes with random effects. Advances in Neural Information Processing Systems 19, to appear, 2006. [[Media:Nips_hMDP_RE.pdf| PDF ]]
# Kim S, Smyth P, Stern H. A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data. To appear in MICCAI, Oct 2-5, 2006.[[Media:InfMM_miccai.pdf| PDF ]]
# Nain D, Haker S, Bobick A, Tannenbaum A. Shape-driven surface segmentation using spherical wavelets. MICCAI, LNCS 4190, Oct 2-5, 2006.
# Styner M, Jomier M, Gerig G: Closed and Open Source Neuroimage Analysis Tools and Libraries at UNC. IEEE Symposium on Biomedical Imaging ISBI. 2006; 702-705. [[Media:ISBI06_Neurolib.pdf| PDF ]]
# Gerig G, Joshi S, Fletcher T, Gorczowski K, Xu S, Pizer SM, Styner M. Statistics of populations of images and its embedded objects: Driving applications in neuroimaging. IEEE Symposium on Biomedical Imaging ISBI. 2006; 1120-1123.[[Media:ISBI06-GerigShape.pdf| PDF]]
# Pieper S, Lorensen W, Schroeder W, Kikinis R. [[Media:Pieper-ISBI-2006-revised.pdf| The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as An Open Platform for the Medical Image Computing Community.]] IEEE Symposium on Biomedical Imaging ISBI. 2006; 698-701.
# Pichon E, Westin CF, and Tannenbaum A. A Hamilton-Jacobi-Bellman approach to high angular diffusion tractography. Proceedings of MICCAI, 2005. [[Media:Pichon-miccai05.pdf| PDF]]
# Yang Y, Zhu L, Haker S, Tannenbaum A. On the harmonic skeleton and vessel data. Proceeedings of MICCAI, 2005. [[Media:YangMICCAI2005.pdf| PDF]]
# Styner M, Gimpel Smith R, Cascio C, Oguz I, Jomier M. Corpus Callosum Subdivision based on a Probabilistic Model of Inter-hemispheric Connectivity. Medical Image Computing and Computer Assisted Interventions MICCAI. 2005 LNCS 3750;765-772. [[Media:MICCAI05CCSubdiv.pdf| PDF]]
# Liu T, Young G, Huang L, Chen N-K, Wong S. “76-space Analysis of Grey Matter Diffusivity: Methods and Applications,” MICCAI 2005.[[Media:MICCAI-2005--461-Liu.pdf| PDF]]
# Pichon E, Tannenbaum A. Curve segmentation using directional information, relation to pattern detection. Proc IEEE International Conference on Image Processing (ICIP), 2005
# Nain D, Haker S, Bobick A, Tannenbaum A. [[Media:Nain.miccai2005.pdf| Multiscale 3D Shape Analysis using Spherical Wavelets]]. Proc MICCAI, LNCS 3750, Oct 26-29 2005; p 459-467.
# Corouge I, Fletcher PT, Joshi S, Gilmore JH, Gerig G. [[Media:Corouge-miccai-2005.pdf| Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis.]] Proc. MICCAI, Oct 26-29 2005; LNCS 3749, pp. 131-139
# Ou W, Golland P. From Spatial Regularization to Anatomical Priors in fMRI Analysis. Proc IPMI, Jul 10-15 2005; LNCS 3565: p 88-100. [[Media:OuGolland_IPMI2005.pdf| PDF]]
# Pohl KM, Fisher J, Levitt JJ, Shenton ME, Kikinis R, Grimson WEL, Wells WM. [[Media:Pohl-miccai-2005.pdf| A Unifying Approach to Registration, Segmentation, and Intensity Correction,]] In Proc. MICCAI 2005: Eigth International Conference on Medical Image Computing and Computer Assisted Intervention, Palm Springs, CA, USA, Springer-Verlag, Part I, vol. 3749 of Lecture Notes in Computer Science, pp. 310-318, 2005
# Pohl KM, Bouix S, Shenton ME, Grimson WEL, Kikinis R. [[Media:Pohl-miccai-short-2005.pdf| Automatic Segmentation Using Non-Rigid Registration,]] In short communications of MICCAI 2005: Eighth International Conference on Medical Image Computing and Computer Assisted Intervention, Palm Springs, CA, USA, 2005
# Martin-Fernandez M, Bouix S, Ungar L, McCarley RW, Shenton ME. Two Methods for Validating Brain Tissue Classifiers. MICCAI 2005, Palm Springs, CA, USA: Duncan J and Gerig G (Eds.): Lecture Notes in Computer Science, volume 3749, pp 515-522, 2005. Springer-Verlag Berlin Heidelberg, 2005. [[Media:Martin-fernandezMICCAI05.pdf| PDF]]
# Kim S, Smyth P, Stern H, Turner J. Parametric response surface models for analysis of multi-site fMRI data. In Proc. MICCAI 2005: Eighth International Conference on Medical Image Computing and Computer Assisted Intervention, Palm Springs, CA, USA, Springer-Verlag, Part I, vol. 3749 of Lecture Notes in Computer Science, pp. 352-359, 2005. [[Media:Seyoung-official.pdf|PDF]]
# O'Donnell L, Westin C-F. White Matter Tract Clustering and Correspondence in Populations. MICCAI 2005, Palm Springs, CA, USA: Duncan J, Gerig G (Eds.): Lecture Notes in Computer Science, volume 3749, pp 140-147, 2005. Springer-Verlag Berlin Heidelberg, 2005. [[Media:OdonnellMICCAI05.pdf| PDF]]
# M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield, Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI. MICCAI05, Palm Spring, CA,: Lecture Notes in Computer Science, volume 3749, pp. 188-195, 2005.
# M. Maddah, W. E. L. Grimson, and S. Warfield, Statistical Modeling and EM Clustering of White Matter Fiber Tracts, ISBI 2006: IEEE 2006 International Symposium on Biomedical Imaging, Arlington, VA, April 6-9, 2006

== Book Chapters ==

# Nain D, Tannenbaum A, Unal G, Yezzi A, Zeitouni O. On a stochastic model of geometric snakes. Mathematical Methods in Computer Vision: A Handbook, edited by Faugeras O, Paragios N, Springer-Verlag, 2005.
# Angenent S, Tannenbaum A, Yezzi A, Zeitouni O. Curve shortening and interacting particle systems. Book chapter in a volume edited by Hamid Krim, 2005.

== Others ==

=== Insight Journal ===

# Goodlett C, Corouge I, Jomier M, Gerig G. [http://hdl.handle.net/1926/39 A Quantitative DTI Fiber Tract Analysis Suite]. Insight Journal, 2005.
# Miller JV. [http://hdl.handle.net/1926/159 "Probability distributions for the Insight Toolkit."] Insight Journal, 2006.
# Gao Y, Melonakos J, Tannenbaum A. [http://hdl.handle.net/1926/225 "Conformal Flattening ITK Filter"] Insight Journal, 2006.
# Melonakos J, Krishnan K, Tannenbaum A. [[Media:Melonakos-ISC2006-bayesian.pdf| An ITK filter for Bayesian segmentation: itkBayesianClassifierImageFilter]]. Insight Journal, Jan 2006. http://hdl.handle.net/1926/160.
# Melonakos J, Al-Hakim R, Fallon J, Tannenbaum A. [[Media:Melonakos-ISC2005-bayesian.pdf| Knowledge-based segmentation of brain MRI scans using the Insight Toolkit]]. Insight Journal, Oct 2005. http://hdl.handle.net/1926/44.

=== Conferences and Workshops ===

# M. Styner, I. Oguz, S. Xu, D. Pantazis, and G. Gerig. Statistical group differences in anatomical shape analysis using hotelling T2 metric. Proc SPIE Medical Imaging Conference, in print, 2007.
# Rathi Y, Olver P, Sapiro G, Tannenbaum A. Affine Invariant Surface Evolutions for 3D Image Segmentation, SPIE 2006
# Rathi Y, Dambreville S, Tannenbaum A. Comparative Analyis of Kernel Methods for Statistical Shape Learning, 2nd Workshop on Computer Vision Approaches to Medical Image Analysis (in conjunction with ECCV) 2006.
# Eric Pichon, Delphine Nain, and Marc Niethammer. [[Media:Pichon-SPIEMI2006-laplace.pdf| A Laplace Equation Approach for Shape Comparison]]. Proc SPIE Medical Imaging, 2006.
# Al-Hakim R, Fallon J, Nain D, Melonakos J, Tannenbaum A. [[Media:Dlpfc-SPIE2006-full.pdf| A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter]]. Proc SPIE Medical Imaging, 2006.
# Pohl KM, Fisher J, Kikinis R, Grimson WEL, Wells WM. [[Media:Pohl-iccv-ws-2005.pdf| Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images,]] In Proc. ICCV 2005: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trend, An International Conference on Computer Vision Workshop, Beijing, China, Springer-Verlag, vol. 3765 of Lecture Notes in Computer Science, 2005
# Zöllei L, Learned-Miller E, Grimson WEL, Wells WM III. Efficient Population Registration of 3D Data. Proc ICCV 2005, Computer Vision for Biomedical Image Applications; Beijing, China
# Angenent S, Pichon E, Tannenbaum A. Mathematical methods in medical imaging. Bulletin of American Mathematical Association, 2006.
# O'Donnell L, Westin C-F. A High-Dimensional Fiber Tract Atlas. accepted to ISMRM 2006.
# Zöllei L, Wells WM III. Multi-modal Image Registration Using Dirichlet-encoded Prior Information. oral presentation at WBIR06
# Fletcher PT, Whitaker RT. Riemannian Metrics on the Space of Solid Shapes. accepted to MICCAI'06 Workshop on Mathematical Foundations of Computational Anatomy (MFCA).
# Cates J, Meyer M, Fletcher PT, Whitaker R. Entropy-Based Particle Systems for Shape Correspondence. accepted to MICCAI'06 Workshop on Mathematical Foundations of Computational Anatomy (MFCA).

=== Submitted and in Preparation ===

# Flashman LA, Roth RM, Pixley HS, Cleavinger HB, Saykin AJ, McAllister TW, Vidaver RM. (submitted). Cavum septum pellucidum in schizophrenia: Clinical and neuropsychological correlates.
# Szymczak A, Tannenbaum A, Stillman A, Mischaikow K. Vessel cores from 3D imagery: a topological approach. submitted to IEEE Trans. Medical Imaging, 2006.
# Michailovich O, Tanenbaum A. Fast approximation of smooth functions from samples of partial derivatives. Submitted for publication to IEEE Signal Processing, 2006.
# Zhu L, Yang Y, Haker S, and Tannenbaum A. An image morphing technique based on optimal mass preserving mapping. Submitted to IEEE Trans. Image Processing, 2006.
# Rathi Y, Vaswani N, Tannenbaum A, Yezzi Y. Tracking Deforming Objects using Particle Filtering for Geometric Active Contours. Submitted to IEEE PAMI, 2005.
# Rathi Y, Tannenbaum A. Kernel PCA for shape based segmentation of medical images. submitted to MICCAI, 2006.
# Gorczowski K, Gerig G, Fletcher T, Pizer SM, Styner M. Statistics of Pose and Shape in Multi-Object Complexes using Principal Geodesic Analysis. submitted to MICCAI, 2006.
# Goodlett C, Davis B, Jean R, Gilmore J, Gerig G. Improved Correspondence for DTI Population Studies via Unbiased Atlas Building. submitted to MICCAI, 2006.
# Corouge I, Fletcher PT, Sarang J, Gouttard S, Gerig G. Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis. submitted MedIA Journal, Jan. 2006
# Gribbin M, Clement M, Muller K, Poe M, Cascio C, Jomier M, Piven J, Gerig G. Statistical analysis of diffusion tensor image data based on first principles, submitted MICCAI'06, March 2006
# Flashman LA, Roth RM, Koven NS, McAllister TW, Vidaver RM, Pendergrass JC. Neural Correlates of Self-Evaluation in Individuals with Schizophrenia. in preparation.
# Basu S, Fletcher PT, Whitaker RT. Rician Noise Removal in Diffusion Tensor MRI, to appear MICCAI'06.
# Han X, Fischl B. Intensity Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms, submitted to IEEE Transactions on Medical Imaging.
# Yu P, Grant P, Qi Y, Han X, Ségonne F, Pienaar R, Busa E, Pacheco J, Makris N, Buckner R, Golland P, Fischl B. Cortical Surface Shape Analysis Based on Spherical Wavelets, submitted to IEEE Transactions on Medical Imaging
# Ségonne F, Pacheco J, Fischl B. Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops, submitted to IEEE Transactions on Medical Imaging
# Voineskos AN, Zai G, Bulgin N, Shaikh S, Lang D, Honer WG, Kennedy JL. MAG but not CNP Gene Associated with Total Brain White Matter. In Preparation. Presented in Part at World Congress of Psychiatric Genetics Annual Meeting. October, 2006.
# Voineskos AN, Bulgin N, Van Adrichem Q, Wong ACH, Lang D, Honer WG, Kennedy JL. MAG but not CNP Gene Associated with Schizophrenia. In Preparation.

Projects:DTIModeling

2007-04-04T19:08:15Z

Mmaddah: /* Publications */

= Publications =

[1] M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson,
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts
to be presented at IPMI 2007, Netherlands.

[2] M. Maddah, W. M. Wells, S. K. Warfield, C-F. Westin, and W. E. L. Grimson,
A Spatial Model of White Matter Fiber Tracts
to be presented at ISMRM 2007, Berlin.

[3] M. Maddah, W. E. L. Grimson, and S. Warfield,
Statistical Modeling and EM Clustering of White Matter Fiber Tracts
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI) 2006, pp. 53-56.

[4] D. Goldberg-Zimring, A. U. J. Mewes, M. Maddah, S. K. Warfield,
Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis
J Neuroimaging, vol. 15, pp. 68S-81S, 2005.

[5] M. Maddah, A. Mewes, S. Haker, W. E. L. Grimson, and S. Warfield,
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.
MICCAI05, Palm Spring, CA, pp. 188-195, 2005.

= Software =

Currently, all the codes are implemented in MATLAB.

Projects:DTIModeling

2007-04-04T18:57:44Z

Mmaddah: