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Deformation Based Features for Alzheimer’s Disease Detection with Linear SVM

  • Alexandre Savio
  • Manuel Grańa
  • Jorge Villanúa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)

Abstract

Detection of Alzheimer’s disease over brain Magnetic Resonance Imaging (MRI) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from VBM studies of the MRI data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature selection is based on the correlation between the scalar values computed from the deformation maps and the control variable. Results with linear kernel SVM reach accuracies comparable to previous best results.

Keywords

Support Vector Machine Feature Vector Mild Cognitive Impairment Magnetic Resonance Image Data Linear Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Manuel Grańa
    • 1
  • Jorge Villanúa
    • 2
  1. 1.Grupo de Inteligencia ComputacionalSpain
  2. 2.Osatek, Hospital Donostia PaseoSan SebastiánSpain

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