干旱区生态环境知识资源中心(Arid): Visual-Saliency-Based Abnormality Detection for MRI Brain Images-Alzheimer's Disease Analysis

Arid

DOI	10.3390/app11199199
	Visual-Saliency-Based Abnormality Detection for MRI Brain Images-Alzheimer's Disease Analysis
	Andrushia, A. Diana; Sagayam, K. Martin; Hien Dang; Pomplun, Marc; Quach, Lien
通讯作者	Dang, H (corresponding author), Thuy Loi Univ, Fac Comp Sci & Engn, Hanoi 100000, Vietnam. ; Dang, H (corresponding author), Univ Massachusetts, Dept Comp Sci, Boston, MA 02125 USA.
来源期刊	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2021
卷号	11 期号:19
英文摘要	In recent years, medical image analysis has played a vital role in detecting diseases in their early stages. Medical images are rapidly becoming available for various applications to solve human problems. Therefore, complex medical features are needed to develop a diagnostic system for physicians to provide better treatment. Traditional methods of abnormality detection suffer from misidentification of abnormal regions in the given data. Visual-saliency detection methods are used to locate abnormalities to improve the accuracy of the proposed work. This study explores the role of a visual saliency map in the classification of Alzheimer's disease (AD). Bottom-up saliency corresponds to image features, whereas top-down saliency uses domain knowledge in magnetic resonance imaging (MRI) brain images. The novelty of the proposed method lies in the use of an elliptical local binary pattern descriptor for low-level MRI characterization. Ellipse-like topologies help to obtain feature information from different orientations. Extensively directional features at different orientations cover the micro patterns. The brain regions of the Alzheimer's disease stages were classified from the saliency maps. Multiple-kernel learning (MKL) and simple and efficient MKL (SEMKL) were used to classify Alzheimer's disease from normal controls. The proposed method used the OASIS dataset and experimental results were compared with eight state-of-the-art methods. The proposed visual saliency-based abnormality detection produces reliable results in terms of accuracy, sensitivity, specificity, and f-measure.
英文关键词	visual saliency MRI brain images classification Alzheimer's disease
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000707846500001
WOS关键词	CLASSIFICATION ; DIAGNOSIS ; YOUNG
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362497
作者单位	[Andrushia, A. Diana; Sagayam, K. Martin] Karunya Inst Sci & Technol, Dept Elect & Commun Engn, Coimbatore 641114, Tamil Nadu, India; [Hien Dang] Thuy Loi Univ, Fac Comp Sci & Engn, Hanoi 100000, Vietnam; [Hien Dang; Pomplun, Marc] Univ Massachusetts, Dept Comp Sci, Boston, MA 02125 USA; [Quach, Lien] Providence VA Med Ctr, Providence, RI 02908 USA
推荐引用方式 GB/T 7714	Andrushia, A. Diana,Sagayam, K. Martin,Hien Dang,et al. Visual-Saliency-Based Abnormality Detection for MRI Brain Images-Alzheimer's Disease Analysis[J],2021,11(19).
APA	Andrushia, A. Diana,Sagayam, K. Martin,Hien Dang,Pomplun, Marc,&Quach, Lien.(2021).Visual-Saliency-Based Abnormality Detection for MRI Brain Images-Alzheimer's Disease Analysis.APPLIED SCIENCES-BASEL,11(19).
MLA	Andrushia, A. Diana,et al."Visual-Saliency-Based Abnormality Detection for MRI Brain Images-Alzheimer's Disease Analysis".APPLIED SCIENCES-BASEL 11.19(2021).