NAMIC Wiki - User contributions [en]

ITK GPAC level set

2010-06-24T23:45:05Z

Palani:

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the current ITK level set class to handle multiphase level sets with support for two, three or a general number of level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can be used to segment any number of objects. We plan to develop a mechanism for multiphase coupling of multichannel data (i.e. nuclear and membrane channels) with scalar or vector-based energy functions. We plan to include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.
</div>

<h3>NA-MIC Progress</h3>
* Finished multiphase implementation and created preliminary test data for validation of results.
* Debugging of multiphase update equations for the general N-level set <math>2^N</math> phase case still continuing.
* Need to add test cases in ITK for specific 4-phase and 8-phase level set evolution.
</div>

==Resources==
* Source code :

We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardoso (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module (possibly)

==References==
*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*K. Palaniappan, F. Bunyak, S. Nath, J. Goffeney, “Parallel processing strategies for cell motility and shape analysis”, High-Throughput Image Reconstruction and Analysis, Ed. C.R. Rao and G. A. Cecchi, Artech, Chapter 3, 2009.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Mosig, S. Jaeger, W. Chaofeng, I. Ersoy, S. K. Nath, K. Palaniappan, S.S. Chen, “Tracking cells in live cell imaging videos using topological alignments”, Algorithms in Molecular Biology, Vol. 4, 10p., 2009.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

*A. Hafiane, F. Bunyak, K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation”, IEEE Int. Conf. Pattern Recognition, 2008.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*I. Ersoy, K. Palaniappan, “Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery”, 30th IEEE Int. Conf. Engineering in Medicine and Biology (EMBC), pp. 371-374, 2008.

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

MedianTexture

2010-06-24T23:32:12Z

Palani:

__NOTOC__
<gallery>
Image:2TS0005_stack0001frame0005.png|Original PCNA-GFP fluorescence confocal microscope image for cell cycle marker studies.
Image:FGPAC_mask_101iter_stack0001frame0005.png|Multiphase GPAC segmentation mask.
Image:Masked-bkg mbp+2.png|Distribution of MBPs shown with each pattern mapped to a unique color.
</gallery>
<gallery>
Image:MBP_2TS0005_5U.png|MBP Uniform pattern 5.
Image:MBP_2TS0005_6U.png|MBP Uniform pattern 6.
Image:MBP_2TS0005_7U.png|MBP Uniform pattern 7.
</gallery>
<gallery>
Image:MBP_2TS0005_8U.png|MBP Uniform pattern 8.
Image:MBP_2TS0005_13U.png|MBP Uniform pattern 13.
Image:MBP_2TS0005_14U.png|MBP Uniform pattern 14.
</gallery>
<gallery>
Image:MBP_2TS0005_15U.png|MBP Uniform pattern 15.
Image:MBP_2TS0005_16U.png|MBP Uniform pattern 16.
Image:MBP_2TS0005_17U.png|MBP Uniform pattern 17.
</gallery>
<gallery>
Image:Mask_2TS0005_-1NU.png|MBP Non-Uniform pattern class.
</gallery>
[[File:MBP-definition.jpg|400px|thumb|left|Definition of MBP]]

==Key Investigators==
* ENSI-Bourges, France: Lucas Menand, Sarah Portugais, Adel Hafiane
* Air Force Research Lab: Guna Seetharaman
* University of Missouri: Filiz Bunyak, K. Palaniappan
* Harvard: Sean Megason

<div style="margin: 20px;">
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Median binary patterns (MBP) are robust feature descriptors for characterizing natural and biological textures. MBPs can be used for cell segmentation, characterizing nuclei and membrane textures and for cell classification.

</div>

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
MBPs is a robust alternative to the local binary pattern (LBP) texture descriptors that uses the local median instead of the central pixel intensity as the reference value to create a binary pattern. MBPs (and LBPs) have the attractive properties of noise-resistance, rotation invariance and shift-invariance and provide a powerful feature set for cell segmentation and classification. Using a 3x3 median window there are nine special median uniform patterns and a set of non-uniform patterns that we assign to a separate category.

</div>

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

<h3>Progress</h3>
We have completed C++ and Matlab implementation of 2D median binary patterns and have a preliminary ITK version that needs to be tested and evaluated for correctness.

</div>

<h3>NA-MIC Week Progress</h3>
* Completed ITK implementation of MBP.
* Histograms of MBPs can be plotted using VTK widgets.
* Remaining work is to build test cases for ITK and write a ITK Journal article.

</div>

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

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as an:
#ITK Module

==References==
*A. Hafiane, G. Seetharaman, K. Palaniappan, B. Zavidovique, “Rotationally invariant hashing of median patterns for texture classification”, Lecture Notes in Computer Science (ICIAR), Vol. 5112, 2008, pp. 619-629. <http://www.ncbi.nlm.nih.gov/pubmed/19116672>

*A. Hafiane, Seetharaman, G., Zavidovique, B.: Median binary pattern for textures classification. Lecture Notes in Computer Science CIAR, 2007, pp. 387–398.

*T. Ojala, Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 2002, pp. 971–987.

</div>

ITK GPAC level set

2010-06-20T23:36:37Z

Palani:

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the current ITK level set class to handle multiphase level sets with support for two, three or a general number of level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can be used to segment any number of objects. We plan to develop a mechanism for multiphase coupling of multichannel data (i.e. nuclear and membrane channels) with scalar or vector-based energy functions. We plan to include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.

</div>
</div>

==Resources==
* Source code :

We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardoso (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module (possibly)

==References==
*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*K. Palaniappan, F. Bunyak, S. Nath, J. Goffeney, “Parallel processing strategies for cell motility and shape analysis”, High-Throughput Image Reconstruction and Analysis, Ed. C.R. Rao and G. A. Cecchi, Artech, Chapter 3, 2009.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Mosig, S. Jaeger, W. Chaofeng, I. Ersoy, S. K. Nath, K. Palaniappan, S.S. Chen, “Tracking cells in live cell imaging videos using topological alignments”, Algorithms in Molecular Biology, Vol. 4, 10p., 2009.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

*A. Hafiane, F. Bunyak, K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation”, IEEE Int. Conf. Pattern Recognition, 2008.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*I. Ersoy, K. Palaniappan, “Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery”, 30th IEEE Int. Conf. Engineering in Medicine and Biology (EMBC), pp. 371-374, 2008.

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

ITK GPAC level set

2010-06-20T22:19:52Z

Palani:

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the level set class to support multiphase level sets with two and three level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can segment any number of objects. Develop a multiphase coupling for multichannel data (i.e. nuclear and membrane channels). Include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.

</div>
</div>

==Resources==
* Source code :

We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardoso (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module (possibly)

==References==
*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*K. Palaniappan, F. Bunyak, S. Nath, J. Goffeney, “Parallel processing strategies for cell motility and shape analysis”, High-Throughput Image Reconstruction and Analysis, Ed. C.R. Rao and G. A. Cecchi, Artech, Chapter 3, 2009.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Mosig, S. Jaeger, W. Chaofeng, I. Ersoy, S. K. Nath, K. Palaniappan, S.S. Chen, “Tracking cells in live cell imaging videos using topological alignments”, Algorithms in Molecular Biology, Vol. 4, 10p., 2009.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

*A. Hafiane, F. Bunyak, K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation”, IEEE Int. Conf. Pattern Recognition, 2008.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*I. Ersoy, K. Palaniappan, “Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery”, 30th IEEE Int. Conf. Engineering in Medicine and Biology (EMBC), pp. 371-374, 2008.

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

MedianTexture

2010-06-20T22:14:17Z

Palani:

__NOTOC__
[[File:MBP-definition.jpg|400px|thumb|left|Definition of MBP]]]
<gallery>
Image:2TS0005_stack0001frame0005.png|Original PCNA-GFP fluorescence confocal microscope image for cell cycle marker studies.
Image:FGPAC_mask_101iter_stack0001frame0005.png|Multiphase GPAC segmentation mask.
Image:Masked-bkg mbp+2.png|Distribution of MBPs shown with each pattern mapped to a unique color.
</gallery>
<gallery>
Image:MBP_2TS0005_5U.png|MBP Uniform pattern 5.
Image:MBP_2TS0005_6U.png|MBP Uniform pattern 6.
Image:MBP_2TS0005_7U.png|MBP Uniform pattern 7.
</gallery>
<gallery>
Image:MBP_2TS0005_8U.png|MBP Uniform pattern 8.
Image:MBP_2TS0005_13U.png|MBP Uniform pattern 13.
Image:MBP_2TS0005_14U.png|MBP Uniform pattern 14.
</gallery>
<gallery>
Image:MBP_2TS0005_15U.png|MBP Uniform pattern 15.
Image:MBP_2TS0005_16U.png|MBP Uniform pattern 16.
Image:MBP_2TS0005_17U.png|MBP Uniform pattern 17.
</gallery>
<gallery>
Image:Mask_2TS0005_-1NU.png|MBP Non-Uniform pattern class.
</gallery>

==Key Investigators==
* ENSI-Bourges, France: Lucas Menand, Sarah Portugais, Adel Hafiane
* Air Force Research Lab: Guna Seetharaman
* University of Missouri: Filiz Bunyak, K. Palaniappan
* Harvard: Sean Megason

<div style="margin: 20px;">
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Median binary patterns (MBP) are robust feature descriptors for characterizing natural and biological textures. MBPs can be used for cell segmentation, characterizing nuclei and membrane textures and for cell classification.

</div>

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
MBPs is a robust alternative to the local binary pattern (LBP) texture descriptors that uses the local median instead of the central pixel intensity as the reference value to create a binary pattern. MBPs (and LBPs) have the attractive properties of noise-resistance, rotation invariance and shift-invariance and provide a powerful feature set for cell segmentation and classification. Using a 3x3 median window there are nine special median uniform patterns and a set of non-uniform patterns that we assign to a separate category.

</div>

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

<h3>Progress</h3>
We have completed C++ and Matlab implementation of 2D median binary patterns and have a preliminary ITK version that needs to be tested and evaluated for correctness. Histograms of MBPs can be plotted using VTK widgets.

</div>

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

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as an:
#ITK Module

==References==
*A. Hafiane, G. Seetharaman, K. Palaniappan, B. Zavidovique, “Rotationally invariant hashing of median patterns for texture classification”, Lecture Notes in Computer Science (ICIAR), Vol. 5112, 2008, pp. 619-629. <http://www.ncbi.nlm.nih.gov/pubmed/19116672>

*A. Hafiane, Seetharaman, G., Zavidovique, B.: Median binary pattern for textures classification. Lecture Notes in Computer Science CIAR, 2007, pp. 387–398.

*T. Ojala, Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 2002, pp. 971–987.

</div>

File:MBP-definition.jpg

2010-06-20T22:09:46Z

Palani: Definition of the median binary pattern feature.

Definition of the median binary pattern feature.

File:Masked-bkg mbp+2.png

2010-06-20T21:56:18Z

Palani: Distribution of MBPs shown with each pattern mapped to a unique color.

Distribution of MBPs shown with each pattern mapped to a unique color.

File:Masked-bkg mbp+2.tif

2010-06-20T21:55:25Z

Palani: Distribution of MBPs shown with each pattern mapped to a unique color.

Distribution of MBPs shown with each pattern mapped to a unique color.

ITK GPAC level set

2010-06-20T21:23:47Z

Palani: /* Resources */

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the level set class to support multiphase level sets with two and three level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can segment any number of objects. Develop a multiphase coupling for multichannel data (i.e. nuclear and membrane channels). Include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.

</div>
</div>

==Resources==
* Source code :

We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardoso (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module

==References==
*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*K. Palaniappan, F. Bunyak, S. Nath, J. Goffeney, “Parallel processing strategies for cell motility and shape analysis”, High-Throughput Image Reconstruction and Analysis, Ed. C.R. Rao and G. A. Cecchi, Artech, Chapter 3, 2009.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Mosig, S. Jaeger, W. Chaofeng, I. Ersoy, S. K. Nath, K. Palaniappan, S.S. Chen, “Tracking cells in live cell imaging videos using topological alignments”, Algorithms in Molecular Biology, Vol. 4, 10p., 2009.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

*A. Hafiane, F. Bunyak, K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation”, IEEE Int. Conf. Pattern Recognition, 2008.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*I. Ersoy, K. Palaniappan, “Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery”, 30th IEEE Int. Conf. Engineering in Medicine and Biology (EMBC), pp. 371-374, 2008.

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

File:Mask 2TS0005 -1NU.png

2010-06-20T20:42:37Z

Palani: MBP histogram of all non-uniform patterns.

MBP histogram of all non-uniform patterns.

File:MBP 2TS0005 17U.png

2010-06-20T20:41:51Z

Palani: MBP Uniform pattern 17 histogram

MBP Uniform pattern 17 histogram

File:MBP 2TS0005 16U.png

2010-06-20T20:41:12Z

Palani: MBP Uniform pattern 16 histogram

MBP Uniform pattern 16 histogram

File:MBP 2TS0005 15U.png

2010-06-20T20:40:46Z

Palani: MBP Uniform pattern 15 histogram

MBP Uniform pattern 15 histogram

File:MBP 2TS0005 14U.png

2010-06-20T20:40:23Z

Palani: MBP Uniform pattern 14 histogram

MBP Uniform pattern 14 histogram

File:MBP 2TS0005 13U.png

2010-06-20T20:39:51Z

Palani: MBP Uniform pattern 13 histogram

MBP Uniform pattern 13 histogram

File:MBP 2TS0005 8U.png

2010-06-20T20:38:13Z

Palani: MBP Uniform pattern 8 histogram

MBP Uniform pattern 8 histogram

File:MBP 2TS0005 7U.png

2010-06-20T20:36:25Z

Palani: MBP Uniform pattern 7 histogram.

MBP Uniform pattern 7 histogram.

File:MBP 2TS0005 6U.png

2010-06-20T20:30:55Z

Palani: MBP Uniform pattern 6 histogram.

MBP Uniform pattern 6 histogram.

File:MBP 2TS0005 5U.png

2010-06-20T20:30:28Z

Palani: MBP Uniform pattern 5 histogram.

MBP Uniform pattern 5 histogram.

File:FGPAC mask 101iter stack0001frame0005.png

2010-06-20T20:07:16Z

Palani: Multiphase GPAC level set segmentation mask for HeLa cell cycle sequence 2TS frame 5.

Multiphase GPAC level set segmentation mask for HeLa cell cycle sequence 2TS frame 5.

File:2TS0005 stack0001frame0005.png

2010-06-20T20:04:21Z

Palani: HeLa cell cycle raw fluorescence image sample from sequence 2TS frame 5.

HeLa cell cycle raw fluorescence image sample from sequence 2TS frame 5.

File:2TS0005 stack0001frame0005.tif

2010-06-20T19:33:26Z

Palani: HeLa cell cycle raw fluorescence image sample from sequence 2TS frame 5.

HeLa cell cycle raw fluorescence image sample from sequence 2TS frame 5.

ITK GPAC level set

2010-06-20T16:37:27Z

Palani:

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the level set class to support multiphase level sets with two and three level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can segment any number of objects. Develop a multiphase coupling for multichannel data (i.e. nuclear and membrane channels). Include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.

</div>
</div>

==Resources==
* Source code :

We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardos (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module

==References==
*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*K. Palaniappan, F. Bunyak, S. Nath, J. Goffeney, “Parallel processing strategies for cell motility and shape analysis”, High-Throughput Image Reconstruction and Analysis, Ed. C.R. Rao and G. A. Cecchi, Artech, Chapter 3, 2009.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Mosig, S. Jaeger, W. Chaofeng, I. Ersoy, S. K. Nath, K. Palaniappan, S.S. Chen, “Tracking cells in live cell imaging videos using topological alignments”, Algorithms in Molecular Biology, Vol. 4, 10p., 2009.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

*A. Hafiane, F. Bunyak, K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation”, IEEE Int. Conf. Pattern Recognition, 2008.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*I. Ersoy, K. Palaniappan, “Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery”, 30th IEEE Int. Conf. Engineering in Medicine and Biology (EMBC), pp. 371-374, 2008.

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

ITK GPAC level set

2010-06-20T16:32:53Z

Palani:

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the level set class to support multiphase level sets with two and three level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can segment any number of objects. Develop a multiphase coupling for multichannel data (i.e. nuclear and membrane channels). Include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.

</div>
</div>

==Resources==
* Source code :

We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardos (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module

==References==
*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*K. Palaniappan, F. Bunyak, S. Nath, J. Goffeney, “Parallel processing strategies for cell motility and shape analysis”, High-Throughput Image Reconstruction and Analysis, Ed. C.R. Rao and G. A. Cecchi, Artech, Chapter 3, 2009.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Mosig, S. Jaeger, W. Chaofeng, I. Ersoy, S. K. Nath, K. Palaniappan, S.S. Chen, “Tracking cells in live cell imaging videos using topological alignments”, Algorithms in Molecular Biology, Vol. 4, 10p., 2009.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

*A. Hafiane, F. Bunyak, K. Palaniappan, “Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation”, IEEE Int. Conf. Pattern Recognition, 2008.

*I. Ersoy, K. Palaniappan, “Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery”, 30th IEEE Int. Conf. Engineering in Medicine and Biology (EMBC), pp. 371-374, 2008.

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

ITK GPAC level set

2010-06-20T16:19:55Z

Palani:

==Key Investigators==
* University of Missouri: Ilker Ersoy, Filiz Bunyak, K. Palaniappan
* Harvard Medical School: Kishore Mosaliganti, Sean Megason

==Project==
[[File:Multiphase-GPAC-HeLa-segmentation.png|400px|thumb|left|4-phase cell segmentation]]
[[File:MRI-multiphase-gpac.png|400px|thumb|left|4-phase MRI segmentation]]
[[File:Histopathology_mvls_4class.png|400px|thumb|left|Vector 4-phase histopathology segmentation]]
[[File:Histopathology_GVD2_grade4_2_zoom.png |400px|thumb|left|Nuclei detection using multiphase for Grade4 prostate carcinoma]]

[[File:Zebrafish-nuclei-membrane-channel-multiphase.png |400px|thumb|left|4-phase nuclei segmentation result using fused (weighted) nuclei plus membrane channels]]
<div style="margin: 20px;">

<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Multiphase level sets efficiently segment multiclass multiobject images. Current ITK level sets support the N-level set approach to handle N-objects. Multiphase methods can be applied to a variety of underlying level set energy functions including Chan-Vese, graph partitioning active contours (GPAC), 4-color level sets, and hybrid approaches.

</div>
<div style="width: 40%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Extend the level set class to support multiphase level sets with two and three level sets cases. Multiphase methods model <math>N</math> classes using <math>\log N</math> phases. Each phase can segment any number of objects. Develop a multiphase coupling for multichannel data (i.e. nuclear and membrane channels). Include support for 2D and 3D multiphase curve and surface evolution.

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

<h3>Progress</h3>
Preliminary multiphase Chan-Vese level set segmentation algorithm has been developed using a Matlab reference implementation. Test cases with synthetic and microscopy imagery have been completed. Need to finish implementation for N-classes, extension to 3D and develop suite of test cases.

</div>
</div>

==Resources==
* Source code :
git@github.com:antonin07130/NAMICSeeding.git
We shall use [http://git-scm.com/ git] for version control :
A small introduction to git : [http://sourceforge.net/apps/trac/gofigure2/wiki/GIT here]
* Data sets:
HeLa cell cycle phase analysis from Cristina Cardos (Technical Univ of Darmstadt).
Prostate carcinoma from Michael Feldman (Univ of Pennsylvania).
Zebrafish embryogenesis 3D confocal multichannel imagery from Sean Megason.
<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered as an:

#ITK Module
#Slicer Module

==References==
*I. Ersoy, F. Bunyak, V. Chagin, M. C. Cardoso, K. Palaniappan, “Segmentation and classification of cell cycle phases in fluorescence imaging”, Lecture Notes in Computer Science (MICCAI), Vol. 5762 (Part II), pp. 617-624, 2009.

*F. Bunyak, K. Palaniappan, V. Chagin, M. C. Cardoso, "Cell Segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns", 31st Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, Minnesota, Sep. 2009, pp. 1424-1428.

*F. Bunyak, A. Hafiane, K. Palaniappan, Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, 2010.

*F. Bunyak, K. Palaniappan, "Level set-based fast graph partitioning active contours using constant memory", Lecture Notes in Computer Science (ACIVS), Vol. 5807, pp. 145-155, 2009.

*A. Hafiane, F. Bunyak, K. Palaniappan, “Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection”, Lecture Notes in Computer Science (ACIVS), Vol. 5259, pp. 903-914, 2008.

*F. Bunyak, K. Palaniappan, "Efficient segmentation using feature-based graph partitioning active contours", 12th IEEE Int. Conf. Computer Vision (ICCV), Sep. 29-Oct. 2 2009, Kyoto, Japan, pp. 873-880.

*S. K. Nath, K. Palaniappan, “Fast graph partitioning active contours for image segmentation using histograms”, EURASIP Journal on Image and Video Processing, 9p., 2009, Article ID 820986 (doi:10.1155/2009/820986).

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

File:Zebrafish-nuclei-membrane-channel-multiphase.png

2010-06-20T16:16:34Z

Palani: Fused multiphase results for zebra fish dual channel fluorescence image slice. Images shown top to bottom include nuclei channel, membrane channel, RGB membrane-nuclear false color, RGB color of [nuclear, membrane enhanced saliency tensor, Mask-blobiness]

Fused multiphase results for zebra fish dual channel fluorescence image slice. Images shown top to bottom include nuclei channel, membrane channel, RGB membrane-nuclear false color, RGB color of [nuclear, membrane enhanced saliency tensor, Mask-blobiness] with background removed, 4-phase results (sal_ch_2nLS_2preproc_0geodesic_0iter_150) without geodesic coupling and final detected nuclei contours using 4-phase on fused (weighted nuclei plus membrane) superimposed on membrane channel (Fused_channels_1nLS_2preprocess_1geodesic_0iter_150).

File:Histopathology GVD2 grade4 2 zoom.png

2010-06-20T15:15:16Z

Palani: Region segmentation and nuclei detection results for a sample Grade 4 image. Blue: Detected nuclei centers, Red: nuclei boundaries obtained using marker controlled watershed segmentation. Nuclei center recall:81%, precision:96%.

Region segmentation and nuclei detection results for a sample Grade 4 image. Blue: Detected nuclei centers, Red: nuclei boundaries obtained using marker controlled watershed segmentation. Nuclei center recall:81%, precision:96%.

File:Histopathology mvls 4class.png

2010-06-20T15:14:06Z

Palani: Multiphase vector-based level sets (MVLS) segmentation of four tissue types into three categories: nuclei (red & black), lumen (green), epithelial cytoplasm (yellow).

Multiphase vector-based level sets (MVLS) segmentation of four tissue types into three categories: nuclei (red & black), lumen (green), epithelial cytoplasm (yellow).

File:MRI-multiphase-gpac.png

2010-06-20T15:02:07Z

Palani: MRI mulitphase GPAC segmentation using 4-phases.

MRI mulitphase GPAC segmentation using 4-phases.

File:Multiphase-GPAC-HeLa-segmentation.png

2010-06-20T14:01:28Z

Palani: Multiphase GPAC level set results for TS2 HeLa cell cycle sequence.

Multiphase GPAC level set results for TS2 HeLa cell cycle sequence.