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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, J.-G., Ma, K.-K., Er, M.-H.: Tumor Segmentation From Magnetic Resonance Imaging By Learning Via One-Class Support Vector Machine. In: Proceeding of International Workshop on Advanced Image Technology (IWAIT 2004), pp. 207–211 (2004)
Kotopoulos, C., Pitas, I.: Segmentation of Ultrasonic Images Using Support Vector Machines. Pattern Recognition Letters 24, 715–727 (2003)
Melgani, F., Bruzzone, L.: Classification of Hyperspectral Remote Sensing Images With Support Vector Machines. IEEE Trans. on Geoscience and Remote Sensing 42, 1778–1790 (2004)
Vapnik, V.: Statistical Learning Theory. John Willey & Sons, New York (1998)
Ratsch, G., Onoda, T., Muller, K.R.: Soft Margins for AdaBoost. Machine Learning 42, 287–320 (2001)
Schohn, G., Cohn, D.: Less is More: Active Learning with Support Vector Machines. In: Proceedings of the Seventeenth International Conference on Machines Learning (ICML2000), pp. 839–846. Morgen Kaugmann, Standord (2000)
Zhan, Y.-Q., Shen, D.-G.: Design Efficient Support Vector Machine for Fast Classification. Pattern Recognition 38, 157–161 (2005)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Lezoray, O., Elmoataz, A., Cardot, H., et al.: Segmentation of color images from serous cytology for automated cell classification. Analytical and Quantitative Cytology and Histology 22, 311–322 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)