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Proceedings Paper

Hippocampal shape analysis: surface-based representation and classification
Author(s): Li Shen; James Ford; Fillia Makedon; Andrew Saykin
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Paper Abstract

Surface-based representation and classification techniques are studied for hippocampal shape analysis. The goal is twofold: (1) develop a new framework of salient feature extraction and accurate classification for 3D shape data; (2) detect hippocampal abnormalities in schizophrenia using this technique. A fine-scale spherical harmonic expansion is employed to describe a closed 3D surface object. The expansion can then easily be transformed to extract only shape information (i.e., excluding translation, rotation, and scaling) and create a shape descriptor comparable across different individuals. This representation captures shape features and is flexible enough to do shape modeling, identify statistical group differences, and generate similar synthetic shapes. Principal component analysis is used to extract a small number of independent features from high dimensional shape descriptors, and Fisher's linear discriminant is applied for pattern classification. This framework is shown to be able to perform well in distinguishing clear group differences as well as small and noisy group differences using simulated shape data. In addition, the application of this technique to real data indicates that group shape differences exist in hippocampi between healthy controls and schizophrenic patients.

Paper Details

Date Published: 15 May 2003
PDF: 12 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480851
Show Author Affiliations
Li Shen, Dartmouth College (United States)
James Ford, Dartmouth College (United States)
Fillia Makedon, Dartmouth College (United States)
Andrew Saykin, Dartmouth Medical School (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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