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

doi:10.1016/j.compmedimag.2022.102057

	Detection of Alzheimer's disease using features of brain region-of-interest-based individual network constructed with the sMRI image
	Feng, Jinwang 1; Zhang, Shao-Wu 1; Chen, Luonan1,2,3,4 ; Zuo, Chunman 2
	2022-06
发表期刊	COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (IF:5.4[JCR-2023],6.1[5-Year])
ISSN	0895-6111
EISSN	1879-0771
卷号	98
DOI	10.1016/j.compmedimag.2022.102057
摘要	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. © 2022 Elsevier Ltd
关键词	Brain Classification (of information) Correlation methods Feature extraction Frequency domain analysis Image segmentation Neurodegenerative diseases Neuroimaging Principal component analysis Radial basis function networks Support vector machines Vectors Alzheimer disease detection Alzheimers disease Brain networks Disease detection Energy feature Energy feature extraction Features extraction Region-of-interest Regions of interest Shearlet transforms
收录类别	EI ; SCIE
语种	英语
出版者	Elsevier Ltd
EI入藏号	20222012109041
EI主题词	Magnetic resonance imaging
EI分类号	461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 701.2 Magnetism: Basic Concepts and Phenomena ; 716.1 Information Theory and Signal Processing ; 723 Computer Software, Data Handling and Applications ; 746 Imaging Techniques ; 903.1 Information Sources and Analysis ; 921.1 Algebra ; 921.3 Mathematical Transformations ; 922.2 Mathematical Statistics
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/183409
专题	生命科学与技术学院_特聘教授组_陈洛南组
通讯作者	Zhang, Shao-Wu; Chen, Luonan
作者单位	1.Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an; 710072, China; 2.Key Laboratory of Systems Biology, Shanghai Institutes of Biochemistry and Cell Biology, Center for Excellence in Molecular Science, Chinese Academy of Sciences, Shanghai; 200031, China; 3.Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou; 310024, China; 4.School of Life Science and Technology, ShanghaiTech University, Shanghai; 201210, China
通讯作者单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Feng, Jinwang,Zhang, Shao-Wu,Chen, Luonan,et al. Detection of Alzheimer's disease using features of brain region-of-interest-based individual network constructed with the sMRI image[J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,2022,98.
APA	Feng, Jinwang,Zhang, Shao-Wu,Chen, Luonan,&Zuo, Chunman.(2022).Detection of Alzheimer's disease using features of brain region-of-interest-based individual network constructed with the sMRI image.COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,98.
MLA	Feng, Jinwang,et al."Detection of Alzheimer's disease using features of brain region-of-interest-based individual network constructed with the sMRI image".COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 98(2022).