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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Pan, C., Yan, X., Zheng, C. (2005). Hard Margin SVM for Biomedical Image Segmentation. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_120
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DOI: https://doi.org/10.1007/11427469_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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