Hard margin SVM for biomedical image segmentation

Authors:
Chen Pan;Xiangguo Yan;Chongxun Zheng
Affiliations:
Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, Shaanxi, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

Biomedical image is often complex. Using SVM for pixel-based segmentation may achieve good results, but training by conventional way always leads to high time cost. In this paper, a novel and real-time training strategy is presented. First, the mean-shift procedures are used to find local modes in RGB 3D histogram. Second, pure samples are selected by the divided modes. Third, the training set is constructed by uniform sampling from the pure samples, so its size can be reduced sharply. In the no-niose case, hard margin criterion replaces soft margin criterion for classification. This strategy constructs an unsupervised support vector classifier. Experimental results demonstrate that the new classifier can achieve accurate results, is more robust to change of the color and faster than watershed algorithm. The new method is suitable to segment blood and bone marrow microscopic images.