Intuitive, Localized Analysis of Shape Variability

Authors:
Paul Yushkevich;Stephen M. Pizer;Sarang C. Joshi;J. S. Marron
Affiliations:
Department of Radiology, University of Pennsylvania, Philadelphia, PA;Medical Image Display & Analysis Group, University of North Carolina, Chapel Hill, NC;Department of Radiation Oncology & Biomedical Engineering, University of North Carolina, Chapel Hill, NC;Department of Statistics, University of North Carolina, Chapel Hill, NC
Venue:
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Year:
2001

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Abstract

Analysis of shape variability is important for diagnostic classification and understanding of biological processes. We present a novel shape analysis approach based on a multiscale medial representation. Our method examines shape variability in separate categories, such as global variability in the coarse-scale shape description and localized variability in the fine-scale description. The method can distinguish between variability in growing and bending. When used for diagnostic classification, the method indicates what shape change accounts for the discrimination and where on the object the change occurs. We illustrate the approach by analysis of 2D clinical corpus callosum shape and discrimination of simulated corpora callosa.