Computer Vision, Graphics, and Image Processing
Automatic Construction of Eigenshape Models by Genetic Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Statistical modeling of shape and appearance using the continuous medial representation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Transformation model and constraints cause bias in statistics on deformation fields
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
An automated approach to connectivity-based partitioning of brain structures
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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Recently, concerns have been raised that the correspondences computed by volumetric registration within homogeneous structures are primarily driven by regularization priors that differ among algorithms. This paper explores the correspondence based on geometric models for one of those structures, mid-sagittal section of the corpus callosum (MSCC), and compared the result with registration paradigms. We use geometric model called continuous medial representation (cm-rep) to normalize anatomical structures on the basis of medial geometry, and use features derived from diffusion tensor tractography for validation. We show that shape-based normalization aligns subregions of the MSCC, defined by connectivity, more accurately than normalization based on volumetric registration. Furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. These results suggest that cm-rep is an appropriate tool for normalizing the MSCC in white matter studies.