title: Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging.
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.
authors.affiliation: School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.|School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.|Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China.
authors.forename: Shuihua
authors.initials: S
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.|Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China.
authors.forename: Yudong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Translational Imaging Division & MRI Unit, Columbia University & New York State Psychiatric Institute, New York, NY, USA.
authors.forename: Ge
authors.initials: G
authors.lastname: Liu
authors.affiliation: School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA.
authors.forename: Preetha
authors.initials: P
authors.lastname: Phillips
authors.affiliation: School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
authors.forename: Ti-Fei
authors.initials: TF
authors.lastname: Yuan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D066264:Datasets as Topic / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male
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