Sparse-Based Morphometry: Principle and Application to Alzheimer’s Disease

  • Pierrick Coupé
  • Charles-Alban Deledalle
  • Charles Dossal
  • Michèle Allard
  • Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9993)

Abstract

The detection of brain alterations is crucial for understanding pathophysiological processes. The Voxel-Based Morphometry (VBM) is one of the most popular methods to achieve this task. Despite its numerous advantages, VBM is based on a highly reduced representation of the local brain anatomy since complex anatomical patterns are reduced to local averages of tissue probabilities. In this paper, we propose a new framework called Sparse-Based Morphometry (SBM) to better represent local brain anatomies. The presented patch-based approach uses dictionary learning to detect anatomical pattern modifications based on their shape and geometry. In our experiences, we compare SBM and VBM along Alzheimer’s Disease (AD) progression.

Keywords

Voxel-based morphometry Alzheimer’s Disease Dictionary learning Abnormality detection Patch-based processing 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Pierrick Coupé
    • 1
    • 2
  • Charles-Alban Deledalle
    • 3
    • 4
  • Charles Dossal
    • 3
    • 4
  • Michèle Allard
    • 5
    • 6
    • 7
  • Alzheimer’s Disease Neuroimaging Initiative
  1. 1.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance
  2. 2.University of Bordeaux, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.University of Bordeaux, IMB, UMR 5251TalenceFrance
  4. 4.CNRS, IMB, UMR 5251TalenceFrance
  5. 5.University of Bordeaux, INCIA, UMR 5287TalenceFrance
  6. 6.CNRS, INCIA, UMR 5287TalenceFrance
  7. 7.EPHEBordeauxFrance

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