Histogram-Based Feature Extraction from Individual Gray Matter Similarity-Matrix for Alzheimer's Disease Classification

• Published in: Journal of Alzheimer's disease Vol. 55; no. 4; p. 1571
• Main Authors: Beheshti, Iman, Maikusa, Norihide, Matsuda, Hiroshi, Demirel, Hasan, Anbarjafari, Gholamreza
• Format: Journal Article
• Language: English
• Published: Netherlands 01.01.2017
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - classification; Alzheimer Disease - diagnosis; Diagnosis, Computer-Assisted; Female; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Neurologic Examination; ROC Curve; White Matter - diagnostic imaging; White Matter - pathology; histogram; individual gray matter; similarity-matrix; Alzheimer’s disease; Fisher criterion
• ISSN: 1387-2877; 1875-8908; 1875-8908
• DOI: 10.3233/JAD-160850

Automatic computer-aided diagnosis (CAD) systems have been widely used in classification of patients who suffer from Alzheimer's disease (AD). This paper presents an automatic CAD system based on histogram feature extraction from single-subject gray matter similarity-matrix for classifying the AD patients from healthy controls (HC) using structural magnetic resonance imaging (MRI) data. The proposed CAD system is composed of five stages. In the first stage, segmentation is employed to perform pre-processing on the MRI images, and segment into gray matter, white matter, and cerebrospinal fluid using the voxel-based morphometric toolbox procedure. In the second stage, gray matter MRI scans are used to construct similarity-matrices. In the third stage, a novel statistical feature-generation process is proposed, utilizing the histogram of the individual similarity-matrix to represent statistical patterns of the respective similarity-matrices of different size and order into fixed-size feature-vectors. In the fourth stage, we propose to combine MRI measures with a neuropsychological test, the Functional Assessment Questionnaire (FAQ), to improve the classification accuracy. Finally, the classification is performed using a support vector machine and evaluated with the 10-fold cross-validation strategy. We evaluated the proposed method on 99 AD and 102 HC subjects from the J-ADNI. The proposed CAD system yields an 84.07% classification accuracy using MRI measures and 97.01% for combining MRI measures with FAQ scores, respectively. The experimental results indicate that the performance of the proposed system is competitive with respect to state-of-the-art techniques reported in the literature.