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

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.