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Alzheimer Disease Classification on Diffusion Weighted Imaging Features

  • M. Termenon
  • A. Besga
  • J. Echeveste
  • A. Gonzalez-Pinto
  • M. Graña
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

An on-going study in Hospital de Santiago Apostol collects anatomical T1-weighted MRI volumes and Diffusion Weighted Imaging (DWI) data of control and Alzheimer’s Disease patients. The aim of this paper is to obtain discriminant features from scalar measures of DWI data, the Fractional Anisotropy (FA) and Mean Diffusivity (MD) volumes, and to train and test classifiers able to discriminate AD patients from controls on the basis of features selected from the FA or MD volumes. In this study, separate classifiers were trained and tested on FA and MD data. Feature selection is done according to the Pearson’s correlation between voxel values across subjects and the control variable giving the subject class (1 for AD patients, 0 for controls). Some of the tested classifiers reach very high accuracy with this simple feature selection process. Those results point to the validity of DWI data as a image-marker for AD.

Keywords

Support Vector Machine Fractional Anisotropy Alzheimer Disease Relevance Vector Machine Anterior Thalamic Radiation 
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

  • M. Termenon
    • 1
  • A. Besga
    • 2
  • J. Echeveste
    • 3
  • A. Gonzalez-Pinto
    • 2
  • M. Graña
    • 1
  1. 1.Grupo de Inteligencia ComputacionalUPV/EHUSpain
  2. 2.Unidad de Investigación en Psiquiatría del Hospital de Santiago ApostolVitoria-GasteizSpain
  3. 3.Departamento de Resonancia MagnéticaOsatek-VitoriaSpain

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