干旱区生态环境知识资源中心(Arid): Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification

Arid

DOI	10.1109/TCBB.2021.3051177
	Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification
	Feng, Jinwang; Zhang, Shao-Wu; Chen, Luonan
通讯作者	Zhang, SW ; Chen, LN
来源期刊	IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN	1545-5963
EISSN	1557-9964
出版年	2022
卷号	19 期号:3 页码:1627-1639
英文摘要	Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification and its prodromal stage-mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git.
英文关键词	Feature extraction Diseases Databases Transforms Alzheimer's disease Image segmentation Frequency-domain analysis Alzheimer's disease image classification regions of interest contourlet transform subband energy feature
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000805807200037
WOS关键词	PRINCIPAL COMPONENT ANALYSIS ; MILD COGNITIVE IMPAIRMENT ; FEATURE-SELECTION ; MRI ; DIAGNOSIS ; PREDICTION ; MACHINE ; PATTERNS ; ATROPHY ; PET
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393074
推荐引用方式 GB/T 7714	Feng, Jinwang,Zhang, Shao-Wu,Chen, Luonan. Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification[J],2022,19(3):1627-1639.
APA	Feng, Jinwang,Zhang, Shao-Wu,&Chen, Luonan.(2022).Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,19(3),1627-1639.
MLA	Feng, Jinwang,et al."Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 19.3(2022):1627-1639.