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Proceedings Paper

SVM algorithm based on wavelet kernel function for medical image segmentation
Author(s): Jun Yang; Jinwen Tian; Jian Liu; Fang Wei
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Paper Abstract

Along with more demand for 3D reconstruction, quantitative analysis and visualization, the more precise segmentation of medical image is required, especially MR head image. But the segmentation of MRI will be much more complex and difficult because of indistinct boundaries between brain tissues due to their overlapping and penetrating with each other, intrinsic uncertainty of MR images induced by heterogeneity of magnetic field, partial volume effect and noise. After studying the kernel function conditions of support vector, we constructed wavelet SVM algorithm based on wavelet kernel function. Its convergence and commonality as well as generalization are analyzed. The comparative experiments are made using the different number of training samples and the different scans, and it .The wavelet SVM can be extended easily and experiment results show that the SVM classifier offers lower computational time and better classification precision and it has good function approximation ability.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971Z (30 October 2009); doi: 10.1117/12.833745
Show Author Affiliations
Jun Yang, Huazhong Univ. of Science and Technology (China)
Wuhan Institute of Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)
Jian Liu, Huazhong Univ. of Science and Technology (China)
Fang Wei, Huazhong Univ. of Science and Technology (China)
Wuhan Institute of Technology (China)


Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)

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