Spatial prior in SVM-based classification of brain images

Rémi Cuingnet; Marie Chupin; Habib Benali; Olivier Colliot

doi:10.1117/12.843983

9 March 2010 Spatial prior in SVM-based classification of brain images

Rémi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76241L (2010) https://doi.org/10.1117/12.843983
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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.

Citation Download Citation

Rémi Cuingnet, Marie Chupin, Habib Benali, and Olivier Colliot "Spatial prior in SVM-based classification of brain images", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241L (9 March 2010); https://doi.org/10.1117/12.843983

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