Deformation Analysis for Shape Based Classification
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Intuitive, Localized Analysis of Shape Variability
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Medial Models Incorporating Object Variability for 3D Shape Analysis
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Hippocampal Shape Analysis Using Medial Surfaces
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
A Unified Framework for MR Based Disease Classification
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Journal of Mathematical Imaging and Vision
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The goal of this work is to develop an approach to shape representation and classification that will allow us to detect and quantify differences in shape of anatomical structures due to various disorders. We used a robust version of skeletons for feature extraction and linear discriminant analysis (the Fisher linear discriminant and the linear Support Vectors method) for classification. We propose a way to map the classification results back into the image domain, interpreting shape differences as a deformation required to bring a shape from one class to the other. An example of analyzing corpus callosum shape in schizophrenia is reported, as well as the results of the study of the statistical properties of the classifier using cross validation techniques.