期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 21, 期 -, 页码 58-73出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2015.05.014
关键词
Magnetic resonance imaging; Multiclass SVM; Kernel SVM; Particle swarm optimization; Time-varying acceleration-coefficient
资金
- NSFC [610011024, 61273243, 51407095]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [13KJB460011, 14KJB520021]
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
- Key Supporting Science and Technology Program (Industry) of Jiangsu Province [BE2012201, BE2013012-2, BE2014009-3]
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
- Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]
- NIH [P50AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584]
Background: We proposed a novel classification system to distinguish among elderly subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC), based on 3D magnetic resonance imaging (MRI) scanning. Methods: The method employed 3D data of 178 subjects consisting of 97 NCs, 57 MCIs, and 24 ADs. First, all these 3D MR images were preprocessed with atlas-registered normalization to form an averaged volumetric image. Then, 3D discrete wavelet transform (3D-DWT) was used to extract wavelet coefficients the volumetric image. The triplets (energy, variance, and Shannon entropy) of all subbands coefficients of 3D-DWT were obtained as feature vector. Afterwards, principle component analysis (PCA) was applied for feature reduction. On the basic of the reduced features, we proposed nine classification methods: three individual classifiers as linear SVM, kernel SVM, and kernel SVM trained by PSO with time-varying acceleration-coefficient (PSOTVAC), with three multiclass methods as Winner-Takes-All (WTA), Max-Wins-Voting, and Directed Acyclic Graph. Results: The 5-fold cross validation results showed that the WTA-KSVM + PSOTVAC performed best over the OASIS benchmark dataset, with overall accuracy of 81.5% among all proposed nine classifiers. Moreover, the method WTA-KSVM + PSOTVAC exceeded significantly existing state-of-the-art methods (accuracies of which were less than or equal to 74.0%). Conclusion: We validate the effectiveness of 3D-DWT. The proposed approach has the potential to assist in early diagnosis of ADs and MCIs. (C) 2015 Elsevier Ltd. All rights reserved.
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