Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease

Journal of Alzheimer's Disease : JAD
Yu-Dong ZhangTi-Fei Yuan

Abstract

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. 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. 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. 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% ...Continue Reading

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Citations

Oct 5, 2017·Revista brasileira de psiquiatria : orgão oficial da Associação Brasileira de Psiquiatria, Asociación Psiquiátrica de la América Latina·Luiz K FerreiraCarlos A Buchpiguel
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Dec 3, 2016·Journal of Alzheimer's Disease : JAD·Tanya GlozmanUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Nov 23, 2017·Frontiers in Molecular Neuroscience·Xin Y ZhangLong J Zhang

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Methods Mentioned

BETA
feature extraction

Software Mentioned

eigenface
GEODAN
GEPSVM
Eigenbrain
Open Access Series of Imaging Studies ( OASIS )
Matlab

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