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

作者：Xiao, Zhe^*; Ding, Yi; Lan, Tian; Zhang, Cong; Luo, Chuanji; Qin, Zhiguang

来源：Computational and Mathematical Methods in Medicine, 2017, 2017: 1952373.

DOI：10.1155/2017/1952373

• MILD COGNITIVE IMPAIRMENT
• SUPPORT VECTOR MACHINES
• VOXEL-BASED MORPHOMETRY
• MATTER CHANGES
• ADNI
• IDENTIFICATION
• RELEVANT
• FEATURES
• VOLUME

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.

• 出版日期2017
• 单位电子科技大学