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Multi-view Classification for Identification of Alzheimer’s Disease

  • Xiaofeng Zhu
  • Heung-Il Suk
  • Yonghua Zhu
  • Kim-Han Thung
  • Guorong Wu
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer’s Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis. The purpose of mapping HOG features onto the space of ROI features is to provide complementary information so that features from different views can not only be comparable (i.e., homogeneous) but also be interpretable. For example, ROI features are robust to noise, but lack of reflecting small or subtle changes, while HOG features are diverse but less robust to noise. The proposed multi-view learning method is designed to learn the transformation between two spaces and to separate the classes under the supervision of class labels. The experimental results on the MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that the proposed multi-view method helps enhance disease status identification performance, outperforming both baseline methods and state-of-the-art methods.

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Xiaofeng Zhu
    • 1
  • Heung-Il Suk
    • 2
  • Yonghua Zhu
    • 3
  • Kim-Han Thung
    • 1
  • Guorong Wu
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  2. 2.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  3. 3.School of Computer, Electronics and InformationGuangxi UniversityNanningChina

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