A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM
	Bao, Min 3; Song GL(宋国立)5,6 ; Huang Z(黄钲)4,5,6; Zhao YW(赵忆文)5,6 ; Zhao XG(赵新刚)5,6 ; Liu YH(刘云辉)3; Han JD(韩建达)2,5,6 ; Li P(李鹏)1
刊名	IEEE Access
	2019
卷号	7 页码:13842-13855
关键词	Modified CLBP PSO-KSVM Glioma detection hybrid features skull removal
ISSN号	2169-3536
产权排序	1
英文摘要	Due to their location, malignant brain tumors are one of humanity's greatest killers, among these tumors, gliomas are the most common. The early detection of gliomas can contribute to the design of proper treatment schemes and, thus, improve the survival rate of patients. However, it is a challenging task to detect the gliomas within the complex structure of the brain. The conventional artificial diagnosis is time-consuming and relies on the clinical experience of radiologists. To detect gliomas more efficiently, this paper proposes a noninvasive automatic diagnosis system for gliomas based on the machine learning methods. First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. Kernel support vector machine (KSVM) combined with the particle swarm optimization is eventually adopted to train classifiers; in this paper, brain magnetic resonance imaging images are labeled with normal, glioma, and other. The experimental results show that the accuracy, sensitivity, and specificity of the proposed method can reach 98.36%, 99.17%, and 97.83%, respectively, which indicates that the proposed method performs better than many current systems.
资助项目	National Natural Science Foundation of China[61703394] ; National Key R&D Program of China[2017YFB1303000]
WOS关键词	FEATURE-EXTRACTION ; MRI ; CLASSIFICATION ; SEGMENTATION ; TUMORS
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
WOS记录号	WOS:000458796400047
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/24250]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Li P(李鹏)
作者单位	1.School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China 2.College of Artificial Intelligence, Nankai University, Tianjin 300071, China 3.Shenjing Hospital, China Medical University, Shenyang 110011, China 4.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 6.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式 GB/T 7714	Bao, Min,Song GL,Huang Z,et al. A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM[J]. IEEE Access,2019,7:13842-13855.
APA	Bao, Min.,Song GL.,Huang Z.,Zhao YW.,Zhao XG.,...&Li P.(2019).A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM.IEEE Access,7,13842-13855.
MLA	Bao, Min,et al."A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM".IEEE Access 7(2019):13842-13855.