Discrimination analysis using multi-object statistics of shape and pose

A main focus of statistical shape analysis is the description of variability of a population of geometric objects. In this paper, we present work towards modeling the shape and pose variability of sets of multiple objects. Principal geodesic analysis (PGA) is the extension of the standard technique of principal component analysis (PCA) into the nonlinear Riemannian symmetric space of pose and our medial m-rep shape description, a space in which use of PCA would be incorrect. In this paper, we discuss the decoupling of pose and shape in multi-object sets using different normalization settings. Further, we introduce methods of describing the statistics of object pose and object shape, both separately and simultaneously using a novel extension of PGA. We demonstrate our methods in an application to a longitudinal pediatric autism study with object sets of 10 subcortical structures in a population of 47 subjects. The results show that global scale accounts for most of the major mode of variation across time. Furthermore, the PGA components and the corresponding distribution of different subject groups vary significantly depending on the choice of normalization, which illustrates the importance of global and local pose alignment in multi-object shape analysis. Finally, we present results of using distance weighted discrimination analysis (DWD) in an attempt to use pose and shape features to separate subjects according to diagnosis, as well as visualize discriminating differences.

[1]  Douglas W. Jones,et al.  Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Guido Gerig,et al.  Elastic model-based segmentation of 3-D neuroradiological data sets , 1999, IEEE Transactions on Medical Imaging.

[3]  Jerry L Prince,et al.  A computerized approach for morphological analysis of the corpus callosum. , 1996, Journal of computer assisted tomography.

[4]  Rikard Berthilsson,et al.  A Statistical Theory of Shape , 1998, SSPR/SPR.

[5]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[6]  D. Louis Collins,et al.  Hippocampal shape analysis using medial surfaces , 2001, NeuroImage.

[7]  P. Thomas Fletcher,et al.  Principal geodesic analysis for the study of nonlinear statistics of shape , 2004, IEEE Transactions on Medical Imaging.

[8]  Martin Styner,et al.  Boundary and Medial Shape Analysis of the Hippocampus in Schizophrenia , 2003, MICCAI.

[9]  Matías N. Bossa,et al.  Statistical Model of Similarity Transformations: Building a Multi-Object Pose , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[10]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[11]  James S. Duncan,et al.  Neighbor-constrained segmentation with level set based 3-D deformable models , 2004, IEEE Transactions on Medical Imaging.

[12]  W. Eric L. Grimson,et al.  Detection and analysis of statistical differences in anatomical shape , 2005, Medical Image Anal..

[13]  Paul A. Yushkevich,et al.  Intuitive, Localized Analysis of Shape Variability , 2001, IPMI.

[14]  W. Clem Karl,et al.  Coupled Shape Distribution-Based Segmentation of Multiple Objects , 2005, IPMI.

[15]  C. Goodall Procrustes methods in the statistical analysis of shape , 1991 .

[16]  U. Grenander,et al.  Hippocampal morphometry in schizophrenia by high dimensional brain mapping. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Paul A. Yushkevich,et al.  Deformable M-Reps for 3D Medical Image Segmentation , 2003, International Journal of Computer Vision.

[18]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[19]  Martin Styner,et al.  Statistical shape analysis of neuroanatomical structures based on medial models , 2003, Medical Image Anal..

[20]  Alan C. Evans,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2000, Nature.

[21]  Andrew Thall,et al.  A method and software for segmentation of anatomic object ensembles by deformable m-reps. , 2005, Medical physics.

[22]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..