By Topic

3D Reconstruction of Head MRI Based on One Class Support Vector Machine with Immune Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Lei Wang ; Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province in Hebei University of Technology, Tianjin, China, 300130. ; Guizhi Xu ; Lei Guo ; Xuena Liu
more authors

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.

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007