NA-MIC Internal Collaborations:StructuralImageAnalysis - Revision history

Marianna at 13:08, 14 May 2010

2010-05-14T13:08:57Z

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Melonakos at 18:41, 4 November 2009

2009-11-04T18:41:20Z

Melonakos at 18:40, 4 November 2009

2009-11-04T18:40:06Z

Melonakos at 18:22, 4 November 2009

2009-11-04T18:22:34Z

Melonakos at 18:18, 4 November 2009

2009-11-04T18:18:01Z

Melonakos: /* Shape Analysis Framework using SPHARM-PDM */

2009-11-04T18:15:33Z

Shape Analysis Framework using SPHARM-PDM

Melonakos: /* Spherical Demons: Fast Surface Registration */

2009-11-04T18:09:51Z

Spherical Demons: Fast Surface Registration

Melonakos: /* Multimodal Atlas */

2009-11-04T18:08:10Z

Multimodal Atlas

Melonakos at 16:46, 2 September 2009

2009-09-02T16:46:28Z

Melonakos at 16:40, 2 September 2009

2009-09-02T16:40:36Z

← Older revision		Revision as of 18:41, 4 November 2009
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	* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) with MANCOVA testing.		* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) with MANCOVA testing.
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−	~~\| \| [[Image:BasePair3DModel.JPG\|200px\|]]~~
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−	~~== [[Projects:NonParametricClustering\|Non Parametric Clustering for Biomolecular Structural Analysis]] ==~~
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−	High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering\|More...]]
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−	~~<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.~~

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← Older revision		Revision as of 18:40, 4 November 2009
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	Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.		Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

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−	~~\| \| [[Image:Circle seg.PNG\|200px]]~~
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−	~~== [[Projects:KPCASegmentation\|Kernel PCA for Segmentation]] ==~~
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−	Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation\|More...]]
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−	<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

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← Older revision		Revision as of 18:22, 4 November 2009
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	<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.		<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

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		+	\| \| [[Image:Cbg-dtiatlas-tracts.png\|200px]]
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		+	== [[Projects:DTIPopulationAnalysis\|Group Analysis of DTI Fiber Tracts]] ==
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		+	Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis\|More...]]
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		+	<font color="red">'''New: '''</font> Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
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 * Shape Analysis Toolkit available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).
 * Slicer 3 module for whole shape analysis pipeline in progress (data access via XNAT, processing via BatchMake and distributed computing using Condor)
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 The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]
-<font color="red">'''New: '''</font> Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.
+<font color="red">'''New: '''</font> H Zhu, H Zhou, J Chen, Y Li, J Lieberman, M Styner, Adjusted Exponentially Tilted Likelihood with Applications to Brain Morphology, Adjusted Exponentially Tilted Likelihood with Applications to Brain Morphology, Biometrics (2009) vol. 65 (3) pp. 919-27.
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 We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]
 <font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.
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 [[Projects:MultimodalAtlas|More...]]
-<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.
+<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, P. Golland, B. Fischl. Task-Optimal Registration Cost Functions. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.
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-| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
+| | [[Image:ICluster_templates.gif|200px]]
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-== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==
+== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==
-Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics.  This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]
+In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
-<font color="red">'''New: '''</font> Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
+<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.
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-| | [[Image:ICluster_templates.gif|200px]]
+| | [[Image:CoordinateChart.png|250px]]
 | |
-== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==
+== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==
-In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
+== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==
-<font color="red">'''NEW: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.
+We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
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 |}

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 {| cellpadding="10" style="text-align:left;"
-| style="width:15%" | [[Image:Circle seg.PNG|200px]]
+| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|250px]]
 | style="width:85%" |
 == [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==