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

Author:

Xiao Zhe1ORCID,Ding Yi1,Lan Tian1,Zhang Cong2,Luo Chuanji1,Qin Zhiguang1

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China

2. China Gas Turbine Establishment, Mianyang, Sichuan 621000, China

Abstract

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.

Funder

NSFC-Guangdong Joint Fund

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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