Anatomical regularization on statistical manifolds for the classification of patients with Alzheimer's disease

Authors:
Rémi Cuingnet;Joan Alexis Glaunès;Marie Chupin;Habib Benali;Olivier Colliot
Affiliations:
Université Pierre et Marie Curie-Paris 6, CNRS, UMR, Inserm, Centre de Recherche de l'Institut Cerveau-Moelle, Paris, France and LIF, Paris, France;Université Pierre et Marie Curie-Paris 6, CNRS, UMR, Inserm, Centre de Recherche de l'Institut Cerveau-Moelle, Paris, France and MAP5, Université Paris 5-René Descartes, Paris, Fran ...;Université Pierre et Marie Curie-Paris 6, CNRS, UMR, Inserm, Centre de Recherche de l'Institut Cerveau-Moelle, Paris, France;Inserm, UMR, LIF, Paris, France;Université Pierre et Marie Curie-Paris 6, CNRS, UMR, Inserm, Centre de Recherche de l'Institut Cerveau-Moelle, Paris, France
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

Citing 3
Cited 0

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Spatially regularized SVM for the detection of brain areas associated with stroke outcome

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I

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Abstract

This paper introduces a continuous framework to spatially regularize support vector machines (SVM) for brain image analysis based on the Fisher metric. We show that, by considering the images as elements of a statistical manifold, one can define a metric that integrates various types of information. Based on this metric, replacing the standard SVM regularization with a Laplace-Beltrami regularization operator allows integrating to the classifier various types of constraints based on spatial and anatomical information. The proposed framework is applied to the classification of magnetic resonance (MR) images based on gray matter concentration maps from 137 patients with Alzheimer's disease and 162 elderly controls. The results demonstrate that the proposed classifier generates less-noisy and consequently more interpretable feature maps with no loss of classification performance.