Distance Metric Learning as Feature Reduction Technique for the Alzheimer’s Disease Diagnosis

  • R. Chaves
  • J. Ramírez
  • J. M. Górriz
  • D. Salas-Gonzalez
  • M. López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

In this paper we present a novel classification method of SPECT images for the development of a computer aided diagnosis (CAD) system aiming to improve the early detection of the Alzheimer’s Disease (AD). The system combines firstly template-based normalized mean square error (NMSE) features of tridimensional Regions of Interest (ROIs) t-test selected with secondly Large Margin Nearest Neighbors (LMNN), which is a distance metric technique aiming to separate examples from different classes (Controls and AD) by a Large Margin. LMNN uses a rectangular matrix (called RECT-LMNN) as an effective feature reduction technique. Moreover, the proposed system evaluates Support Vector Machine (SVM) classifier, yielding a 97.93% AD diagnosis accuracy, which reports clear improvements over existing techniques, for instance the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Normalized Minimum Squared Error (NMSE) evaluated with SVM.

Keywords

SPECT Brain Imaging Alzheimer’s disease Distance Metric Learning feature reduction Support Vector Machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. Chaves
    • 1
  • J. Ramírez
    • 1
  • J. M. Górriz
    • 1
  • D. Salas-Gonzalez
    • 1
  • M. López
    • 1
  1. 1.University of GranadaGranadaSpain

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