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Morphometry of the hippocampus based on a deformable model and support vector machines

Title
Morphometry of the hippocampus based on a deformable model and support vector machines
Authors
Kim, JSKim, YGChoi, SMKim, MH
Ewha Authors
김명희
SCOPUS Author ID
김명희scopus
Issue Date
2005
Journal Title
ARTIFICIAL INTELLIGENCE IN MEDICINE, PROCEEDINGS
ISSN
0302-9743JCR Link
Citation
vol. 3581, pp. 353 - 362
Publisher
SPRINGER-VERLAG BERLIN
Indexed
SCOPUS WOS
Abstract
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.
ISBN
3-540-27831-1
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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