title: 3D reconstruction of head MRI based on one class support vector machine with immune algorithm.
abstract: Due to complexity and irregulation of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image has been a hot area. Support vector machine (SVM) based on statistical learning theory is mainly utilized in classification and regression. One Class SVM (OCSVM) was originally proposed for solving some special classification problems. In this paper, OCSVM, which tries to find the smallest hypersphere enclosing target data in high dimensional space by kernel function, is firstly explored into the application to 3D reconstruction. However, selecting parameters for OCSVM is a complicated problem. In order to reduce the blindness of parameter selection and perfect SVM theory, Immune Algorithm (IA) and K-fold cross validation are introduced to intelligently search optimal parameter. The experimental results demonstrate OCSVM is effective with high reconstruction accuracy.
authors.affiliation: Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province in Hebei University of Technology, Tianjin, China, 300130.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.forename: Guizhi
authors.initials: G
authors.lastname: Xu
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Xuena
authors.initials: X
authors.lastname: Liu
authors.forename: Shuo
authors.initials: S
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006257:Head / Q000033:anatomy & histology*; 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