30 October 2009 SVM algorithm based on wavelet kernel function for medical image segmentation
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Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74971Z (2009) https://doi.org/10.1117/12.833745
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jun Yang, Jun Yang, Jinwen Tian, Jinwen Tian, Jian Liu, Jian Liu, Fang Wei, Fang Wei, } "SVM algorithm based on wavelet kernel function for medical image segmentation", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971Z (30 October 2009); doi: 10.1117/12.833745; https://doi.org/10.1117/12.833745
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