Anatomical Regularization on Statistical Manifolds for the Classification of Patients with Alzheimer’s Disease

  • Rémi Cuingnet
  • Joan Alexis Glaunès
  • Marie Chupin
  • Habib Benali
  • Olivier Colliot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rémi Cuingnet
    • 1
    • 2
  • Joan Alexis Glaunès
    • 1
    • 3
  • Marie Chupin
    • 1
  • Habib Benali
    • 2
  • Olivier Colliot
    • 1
  1. 1.CNRS UMR 7225, Inserm UMR_S 975, Centre de Recherche de l’Institut Cerveau-Moelle (CRICM)Université Pierre et Marie Curie-Paris 6ParisFrance
  2. 2.UMR_S 678, LIFInsermParisFrance
  3. 3.MAP5Université Paris 5 - René DescartesParisFrance

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