Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
A fast depth-buffer-based voxelization algorithm
Journal of Graphics Tools
Three Dimensional MR-Based Morphometric Comparison of Schizophrenic and Normal Cerebral Ventricles
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Automatic and Robust Computation of 3D Medial Models Incorporating Object Variability
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
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This paper presents an effective representation scheme for the statistical shape analysis of the hippocampal structure and its shape classification: Morphometry of the hippocampus. The deformable model based on FEM (Finite Element Method) and ICP (Iterative Closest Point) algorithm allows us to represent parametric surfaces and to normalize multi-resolution shapes. Such deformable surfaces and 3D skeletons extracted from the voxel representations are stored in the Octree data structure. And, it will be used for the hierarchical shape analysis. We have trained SVM (Support Vector Machine) for classifying between the control and patient groups. Results suggest that the presented representation scheme provides various level of shape representation and SVM can be a useful classifier in analyzing the statistical shape of the hippocampus.