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Proceedings Paper

Spatial prior in SVM-based classification of brain images
Author(s): Rémi Cuingnet; Marie Chupin; Habib Benali; Olivier Colliot
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Paper Abstract

This paper introduces a general framework for spatial prior in SVM-based classification of brain images based on Laplacian regularization. Most existing methods include spatial prior by adding a feature aggregation step before the SVM classification. The problem of the aggregation step is that the individual information of each feature is lost. Our framework enables to avoid this shortcoming by including the spatial prior directly in the SVM. We demonstrate that this framework can be used to derive embedded regularization corresponding to existing methods for classification of brain images and propose an efficient way to implement them. This framework is illustrated on the classification of MR images from 55 patients with Alzheimer's disease and 82 elderly controls selected from the ADNI database. The results demonstrate that the proposed algorithm enables introducing straightforward and anatomically consistent spatial prior into the classifier.

Paper Details

Date Published: 9 March 2010
PDF: 9 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241L (9 March 2010); doi: 10.1117/12.843983
Show Author Affiliations
Rémi Cuingnet, Univ. Pierre et Marie Curie (France)
CNRS, UMR 7225, CRICM (France)
INSERM, UMRS 975, CRICM (France)
Marie Chupin, Univ. Pierre et Marie Curie (France)
CNRS, UMR 7225, CRICM (France)
INSERM, UMRS 975, CRICM (France)
Habib Benali, INSERM, UMRS 678, LIF (France)
Olivier Colliot, Univ. Pierre et Marie Curie (France)
CNRS, UMR 7225, CRICM (France)
INSERM, UMRS 975, CRICM (France)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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