Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. (August 2015)

• Record Type: Journal Article
• Authors: Zhang, Yudong; Wang, Shuihua; Phillips, Preetha; Dong, Zhengchao; Ji, Genlin; Yang, Jiquan
• Is Part Of: Biomedical signal processing and control. Volume 21(2015)
• Page Start: 58
• Page End: 73
• Publication Date: 2015-08
• Subjects: Magnetic resonance imaging -- Multiclass SVM -- Kernel SVM -- Particle swarm optimization -- Time-varying acceleration-coefficient; (D)(F)WT (discrete) (fast) wavelet transform -- (M)(K)SVM (multiclass) (kernel) support vector machine -- 1D/2D/3D-DWT one/two/three dimensional DWT -- AD Alzheimer's disease -- ALS alternating least squares -- ASF atlas scaling factor -- CA classification accuracy -- CDR clinical Dementia rating -- CM confusion matrix -- CV cross validation -- DAG Directed Acyclic Graph -- DS down-sampling -- eTIV estimated total intracranial volume -- GA genetic algorithm -- KNN k-Nearest neighbor -- MCI mild cognitive impairment -- MMSE mini-mental state examination -- MR(I) magnetic resonance (imaging) -- MSE median square error -- MWV Max-Wins-Voting -- NC normal control -- nWBV normalized whole brain volume -- OASIS open access series of imaging studies -- PC(A) principal component (analysis) -- PSO particle swarm optimization -- QP quadratic programming -- RBF radial basis function -- ROI region of interest -- RS random search -- SA simulated annealing -- SES socio-economic status -- SMO sequential minimal optimization -- SVMDT SVM decision tree -- TN/FN(R) true/false negative (rate) -- TP/FP(R) true/false positive (rate) -- TVAC time-varying acceleration coefficient -- WTA Winner-Takes-All; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.05.014
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: We take the whole brain (not the ROIs) as the research objective, so there is no need for brain segmentation. The sensitivity of NC is up to 93.81%. The specificities of MCI and AD are 93.39% and 92.21%. We use 3D-DWT to capture the 3D texture feature of brain, use ALS-PCA for feature reduction of dataset containing missing attributes. We use TVAC-PSO to get the optimal kernel parameter of each individual KSVM. We compare three different multiclass KSVM methods, and find that WTA performs best. Abstract: 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.