Yudong Zhang1,2, Shuihua Wang1, Preetha Phillips3, Jiquan Yang4, Ti-Fei Yuan1. 1. School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China. 2. Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, Guilin, Guangxi, China. 3. School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA. 4. Jiangsu Key Laboratory of 3d Printing Equipment And Manufacturing, Nanjing, Jiangsu, China.
Abstract
BACKGROUND: Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance. OBJECTIVE: Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images. METHODS: We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel. RESULTS: The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature. CONCLUSIONS: We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.
BACKGROUND: Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance. OBJECTIVE: Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images. METHODS: We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel. RESULTS: The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature. CONCLUSIONS: We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.
Entities:
Keywords:
Alzheimer’s disease; classification; detection; eigenbrain; machine learning; magnetic resonance imaging; polynomial kernel; prediction; support vector machine
Authors: Shuihua Wang; Ming Yang; Sidan Du; Jiquan Yang; Bin Liu; Juan M Gorriz; Javier Ramírez; Ti-Fei Yuan; Yudong Zhang Journal: Front Comput Neurosci Date: 2016-10-19 Impact factor: 2.380
Authors: Luiz K Ferreira; Jane M Rondina; Rodrigo Kubo; Carla R Ono; Claudia C Leite; Jerusa Smid; Cassio Bottino; Ricardo Nitrini; Geraldo F Busatto; Fabio L Duran; Carlos A Buchpiguel Journal: Braz J Psychiatry Date: 2017-10-02 Impact factor: 2.697