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Jun Yang

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Proceedings Article | 30 October 2009 Paper

SVM algorithm based on wavelet kernel function for medical image segmentation

Jun Yang, Jinwen Tian, Jian Liu, Fang Wei

Proceedings Volume 7497, 74971Z (2009) https://doi.org/10.1117/12.833745

KEYWORDS: Image segmentation, Wavelets, Medical imaging, Magnetic resonance imaging, Image processing algorithms and systems, Neuroimaging, Brain, Head, Tissues, Wavelet transforms

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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.

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