Alzheimer's disease classification using features extracted from nonsubsampled contourlet subband-based individual networks

Morphological networks constructed with structural magnetic resonance imaging (sMRI) images have been widely investigated by exploring interregional alterations of different brain regions of interest (ROI) in the spatial domain for Alzheimer's disease (AD) classification. However, few attentions are attracted to construct a subband-based individual network with the sMRI image in the frequency domain. In order to verify the feasibility of constructing individual networks with subbands and extract features from the subband-based individual network for AD classification, in this study, we propose a novel method to capture correlations of the abnormal energy distribution patterns related to AD by constructing nonsubsampled contourlet subband-based individual networks (NCSINs) in the frequency domain. Specifically, a 2-dimensional representation of the preprocessed sMRI image is firstly reshaped by downsampling and reconstruction steps. Then, the nonsubsampled contourlet transform is performed on the 2-dimensional representation to obtain directional subbands, and each directional subband at one scale is described by a column energy feature vector (CV) regarded as a node of the NCSIN. Subsequently, edge between any two nodes is weighted with connection strength (CS). Finally, the concatenation of node and edge features of the NCSINs at different scales is used as a network feature of the sMRI image for AD classification. Meanwhile, the support vector machine (SVM) classifier with a radial basis function (RBF) kernel is applied for categorizing 680 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that it is feasible to construct the subband-based individual network in the frequency domain and also show that our NCSIN method outperforms five other state-of-the-art approaches. © 2020 Elsevier B.V.