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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 1952373, 13 pages
Research Article

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
2China Gas Turbine Establishment, Mianyang, Sichuan 621000, China

Correspondence should be addressed to Zhe Xiao; nc.ude.ctseu.dts@oaix.ehz

Received 14 December 2016; Revised 15 March 2017; Accepted 2 April 2017; Published 22 May 2017

Academic Editor: John Mitchell

Copyright © 2017 Zhe Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a novel classification framework to precisely identify individuals with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.