NDLI: Classification of sMRI data in Alzheimer's disease based on UMPCA and Laplacian Score

Content Provider	IEEE Xplore Digital Library
Author	Chunlu Lai Ju Liu Qiang Wu
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China (Chunlu Lai; Ju Liu; Qiang Wu)
Abstract	Classification of structural magnetic resonance imaging (sMRI) brain scans is helpful to detect Alzheimer's disease (AD) at its early stage. In this paper we present a classification scheme that combines the uncorrelated multilinear principal component analysis (UMPCA) and Laplacian Score (LS) methods, which are known to be effective to the structural correlation preserving and redundancy reduction of the AD-related features hidden in the sMRI. In this scheme, UMPCA is first employed to extract features directly from the tensorial sMRI data of AD subjects and healthy control (HC) subjects. Then, Laplacian Score is used to select the more discriminative features by evaluating their power of locality preserving. Finally, an SVM classifier is built to distinguish AD patients from HC subjects. Experimental results demonstrate that the UMPCA-LS-based method achieves higher recognition accuracy than existing methods.
Starting Page	1
Ending Page	4
File Size	151756
Page Count	4
File Format	PDF
e-ISBN	9781479904341
DOI	10.1109/ICICS.2013.6782801
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Support vector machines Tensile stress Laplace equations Accuracy Training Vectors support vector machine Alzheimer's disease uncorrelated multilinear principal component analysis Laplacian Score structural magnetic resonance imaging
Content Type	Text
Resource Type	Article

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