Detection of Alzheimer’s Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging

Article type: Research Article

Authors: Wang, Shuihua^{a; b; c; 1} | Zhang, Yudong^{a; c; *; 1} | Liu, Ge^d | Phillips, Preetha^e | Yuan, Ti-Fei^{a; *}

Affiliations: [a] School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China | [b] School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China | [c] Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China | [d] Translational Imaging Division & MRI Unit, Columbia University & New York State Psychiatric Institute, New York, NY, USA | [e] School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA

Correspondence: [*] Correspondence to: Yudong Zhang and Ti-Fei Yuan, 1 Wenyuan, Nanjing, Jiangsu 210023, China. Tel.: +86 15905183664; E-mails: zhangyudong@njnu.edu.cn (Yudong Zhang), ytf0707@126.com (Ti-Fei Yuan).

Note: [1] These authors contributed equally to this work.

Abstract: Background:Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer’s disease (AD) in its early stages. Objective:However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method. Methods:In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student’s t-test, and Welch’s t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis. Results:The results showed that “3D-DF+WTT+TSVM” achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications. Conclusions:The 3D-DF is effective in AD subject and related region detection.

Keywords: Alzheimer’s disease, computer vision, displacement field, generalized eigenvalue proximal support vector machine, machine learning, magnetic resonance imaging, pattern recognition, twin support vector machine

DOI: 10.3233/JAD-150848

Journal: Journal of Alzheimer's Disease, vol. 50, no. 1, pp. 233-248, 2016