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Classification of Alzheimer’s Disease Using a Self-Smoothing Operator

  • Juan Eugenio Iglesias
  • Jiayan Jiang
  • Cheng-Yi Liu
  • Zhuowen Tu
  • the Alzheimers Disease Neuroimaging Initiative
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

In this study, we present a system for Alzheimer’s disease classification on the ADNI dataset [1]. Our system is able to learn/fuse registration-based (matching) and overlap-based similarity measures, which are enhanced using a self-smoothing operator (SSO). From a matrix of pair-wise affinities between data points, our system uses a diffusion process to output an enhanced matrix. The diffusion propagates the affinity mass along the intrinsic data space without the need to explicitly learn the manifold. Using the enhanced metric in nearest neighborhood classification, we show significantly improved accuracy for Alzheimer’s Disease over Diffusion Maps [2] and a popular metric learning approach [3]. State-of-the-art results are obtained in the classification of 120 brain MRIs from ADNI as normal, mild cognitive impairment, and Alzheimer’s.

Keywords

Support Vector Machine Feature Selection Mild Cognitive Impairment Brain Magnetic Resonance Imaging Medical Image Analysis 
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

  • Juan Eugenio Iglesias
    • 1
  • Jiayan Jiang
    • 1
  • Cheng-Yi Liu
    • 1
  • Zhuowen Tu
    • 1
  • the Alzheimers Disease Neuroimaging Initiative
  1. 1.Laboratory of Neuro ImagingUniversity of CaliforniaLos AngelesUSA

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