Reference

1. Cristianini, N., and J. Shawe-Taylor. “An Introduction to support vector machines and other kernel-based learning methods” New York: Cambridge University Press, 2000. 
2. Vapnik, V. N. “The Ature of Statistical Learning Theory” New York: Springer, 1995. 
3. A. Madabhushi, “Digital pathology image analysis: opportunities and challenges,” Imaging in Medicine, vol. 1, no. 1, pp. 7– 10, 2009. 
4. A. N. Esgiar, R. N. G. Naguib, B. S. Sharif, M. K. Bennett, and A. Murray, “Fractal analysis in the detection of colonic cancer images,” IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 54–58, 2002. 
5. L. Yang, O. Tuzel, P. Meer, and D. J. Foran, “Automatic image analysis of histopathology specimens using concave vertex graph,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008, pp. 833– 841, Springer, Berlin, Germany, 2008. 
6. R. C. Gonzalez, Digital Image Processing, Pearson Education India, 2009.
7. S. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 1107–1118, 2009. 
8. J. C. Caicedo, A. Cruz, and F. A. Gonzalez, “Histopathology image classification using a bag of features and kernel functions,” in Artificial Intelligence in Medicine, vol. 5651 of Lecture Notes in Computer Science, pp. 126–135, Springer, Berlin, Germany, 2009. 
9. H. S. Wu, J. Barba, and J. Gil, “Iterative thresholding for segmentation of cells from noisy images” Journal of Microscopy, vol.197, no. 3, pp. 296–304, 2000.