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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, JS., Kim, YG., Choi, SM., Kim, MH. (2005). Morphometry of the Hippocampus Based on a Deformable Model and Support Vector Machines. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_48
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DOI: https://doi.org/10.1007/11527770_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
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