Bag of Features for Automatic Classification of Alzheimer’s Disease in Magnetic Resonance Images

  • Andrea Rueda
  • John Arevalo
  • Angel Cruz
  • Eduardo Romero
  • Fabio A. González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

The goal of this paper is to evaluate the suitability of a bag-of-feature representation for automatic classification of Alzheimer’s disease brain magnetic resonance (MR) images. The evaluated method uses a bag-of-features (BOF) to represent the MR images, which are then fed to a support vector machine, which has been trained to distinguish between normal control and Alzheimer’s disease. The method was applied to a set of images from the OASIS data set. An exhaustive exploration of different BOF parameters was performed, i.e. feature extraction, dictionary construction and classification model. The experimental results show that the evaluated method reaches competitive performance in terms of accuracy, sensibility and specificity. In particular, the method based on a BOF representation outperforms the best published result in this data set improving the equal error classification rate in about 10% (0.80 to 0.95 for Group 1 and 0.71 to 0.81 for Group 2).

Keywords

Support Vector Machine Patch Size Visual Word Dictionary Size Visual Dictionary 
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 2012

Authors and Affiliations

  • Andrea Rueda
    • 1
  • John Arevalo
    • 1
  • Angel Cruz
    • 1
  • Eduardo Romero
    • 1
  • Fabio A. González
    • 1
  1. 1.BioIngenium Research GroupUniversidad Nacional de ColombiaBogotáColombia

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