Brain MR Image Classification for Alzheimer's Disease Diagnosis Based on Multifeature Fusion. (22nd May 2017)

• Record Type: Journal Article
• Authors: Xiao, Zhe; Ding, Yi; Lan, Tian; Zhang, Cong; Luo, Chuanji; Qin, Zhiguang
• Other Names: Mitchell John Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2017(2017)
• Publication Date: 2017-05-22
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2017/1952373
• Languages: English
• ISSNs: 1748-670X

Abstract

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.