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

Content Provider	IEEE Xplore Digital Library
Author	Lei Wang Guizhi Xu Lei Guo Xuena Liu Shuo Yang
Copyright Year	2007
Description	Author affiliation: Hebei Univ. of Technol., Tianjin (Lei Wang; Guizhi Xu; Lei Guo; Xuena Liu; Shuo Yang)
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.
Starting Page	6015
Ending Page	6018
File Size	571587
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4353719
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic heads Magnetic resonance imaging Support vector machines Support vector machine classification Image reconstruction Kernel Rendering (computer graphics) Statistical learning Image processing Visualization
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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