A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation

Author

Du, Xinyu ; Li, Yongjie ; Yao, Dezhong

Author_Institution

Center of Neuro-Inf., Univ. of Electron. Sci. & Technol. of China, Chengdu

Volume

3

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

49

Lastpage

53

Abstract

In this work, we propose a kind of supervised classification - support vector machine (SVM) to segment magnetic resonance image (MRI). As a classifier, SVM can employ structural risk minimization principle and perform better in classification task. Based on those excellent capabilities of SVM, we conduct many detailed experiments on some standard simulated data and real data. According to the experiments results, SVM is proven to be a good classifier in MRI segmentation.

Keywords

biomedical MRI; image segmentation; medical image processing; minimisation; pattern classification; support vector machines; magnetic resonance image segmentation; structural risk minimization principle; supervised classification; support vector machine; Biomedical imaging; Clustering algorithms; Image edge detection; Image segmentation; Magnetic resonance; Magnetic resonance imaging; Medical diagnostic imaging; Risk management; Support vector machine classification; Support vector machines; MRI; SVM; Segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.400

Filename

4667099