title: Research on the segmentation of MRI image based on multi-classification support vector machine.
abstract: In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. As a new kind of machine learning, Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) has high generalization ability, especially for dataset with small number of samples in high dimensional space. SVM was originally developed for two-class classification. It is extended to solve multi-class classification problem. In this paper, 57 dimensional feature vectors for MRI image are selected as input for SVM. The segmentation of MRI image based on the Multi-Classification SVM (MCSVM) is investigated. As our experiment demonstrates, the boundaries of 7 kinds of encephalic tissues are extracted successfully, and it can reach satisfactory generalization accuracy. Thus, SVM exhibits its great potential in image segmentation.
authors.affiliation: Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China. guoshengrui@163.com
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Xuena
authors.initials: X
authors.lastname: Liu
authors.forename: Youxi
authors.initials: Y
authors.lastname: Wu
authors.forename: Weili
authors.initials: W
authors.lastname: Yan
authors.forename: Xueqin
authors.initials: X
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D018448:Models, Immunological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity