Detection of Alzheimer's disease using features of brain region-of-interest-based individual network constructed with the sMRI image. (June 2022)

• Record Type: Journal Article
• Authors: Feng, Jinwang; Zhang, Shao-Wu; Chen, Luonan; Zuo, Chunman
• Is Part Of: Computerized medical imaging and graphics. Volume 98(2022)
• Publication Date: 2022-06
• Subjects: Alzheimer's disease detection -- Magnetic resonance imaging -- Shearlet transform -- Brain network -- Energy feature extraction; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102057
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Brain networks constructed with regions of interest (ROIs) from the structural magnetic resonance imaging (sMRI) image are widely investigated for detecting Alzheimer's disease (AD). However, the ROI is generally represented by spatial domain-based features, so attentions are hardly paid to constructing a brain network with the frequency domain-based feature. In order to accurately characterize the ROI in the frequency domain and then construct an individual network, in this study, a novel method, which can describe the ROI properly by directional subbands and capture correlations between those ROIs, is proposed to construct a shearlet subband energy feature-based individual network (SSBIN) for AD detection. Specifically, the SSBIN is constructed with 90 ROIs which are segmented from the pre-processed sMRI image based on the automated anatomical labeling atlas, the 90 ROIs are represented by directional subband-based energy feature vectors (SVs) formed by jointing energy features extracted from their directional subbands, and the weight values of the SSBIN are computed by Pearson's correlation coefficient (PCC). Subsequently, two network features are extracted from the SSBIN: the node feature vector (NV) is computed by averaging the 90 SVs; the low dimensional edge feature vector (LV) is obtained by kernel principal component analysis (KPCA). Following that the concatenation of NV and LV is used as a SSBIN-based feature for the sMRI image. Finally, we use support vector machine (SVM) with the radial basis function kernel as classifier to categorize 680 subjects selected from the AD Neuroimaging Initiative (ADNI) database. Experimental results validate that the ROI can be properly characterized by the NV, and correlations between ROIs captured by the LV play an important role in AD detection. Besides, a series of comparisons with four current state-of-the-art approaches demonstrate the higher AD detecting performance of the SSBIN method. Highlights: A ROI-based individual brain network in the frequency domain is constructed for AD detection. The shearlet is used to transform the ROI so that energy features extracted are explainable. The SSBIN feature, containing information of the ROI and their correlation, can describe the sMRI well.