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A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM
Song GL(宋国立)1,2; Huang Z(黄钲)1,2,3; Zhao YW(赵忆文)1,2; Zhao XG(赵新刚)1,2; Liu, Yunhui4; Bao, Min4; Han JD(韩建达)1,2,5; Li P(李鹏)6
Department机器人学研究室
Source PublicationIEEE Access
ISSN2169-3536
2019
Volume7Pages:13842-13855
Indexed BySCI ; EI
EI Accession number20190806530099
WOS IDWOS:000458796400047
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China
KeywordModified CLBP PSO-KSVM Glioma detection hybrid features skull removal
AbstractDue 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.
Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordFEATURE-EXTRACTION ; MRI ; CLASSIFICATION ; SEGMENTATION ; TUMORS
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Natural Science Foundation of China[61703394] ; National Key R&D Program of China[2017YFB1303000]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24250
Collection机器人学研究室
Corresponding AuthorLi P(李鹏)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
4.Shenjing Hospital, China Medical University, Shenyang 110011, China
5.College of Artificial Intelligence, Nankai University, Tianjin 300071, China
6.School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Recommended Citation
GB/T 7714
Song GL,Huang Z,Zhao YW,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 Song GL.,Huang Z.,Zhao YW.,Zhao XG.,Liu, Yunhui.,...&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 Song GL,et al."A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM".IEEE Access 7(2019):13842-13855.
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