Segmentation of medical images is very important nowadays since the images for diagnosis by Radiologist are huge in number. In this paper, texture based segmentation algorithms are considered for comparison. The problem with some of these methods is, they need human interaction for accurate and reliable segmentation. Segmentation based on Gray level co-occurrence matrix gives better result for variance but computational complexity is more. Watershed has less complexity but gives over segmentation. Segmentation using Kekre’s Median Codebook Generation (KMCG) and Kekre’s Fast Codebook Generation (KFCG) algorithm show proper tumor demarcation by avoiding other part of the image.

1. M. Shenton, R. Kikinis, F. Jolesz, et al. Abnormalities of the Left Temporal Lobe and Thought Disorder in Schizophrenia. N. Engl. J. Med,vol.327, pp. 604 - 612, August 1992.
2. K. Hohne et al. A framework for the Generation of 3D Anatomical Atlases. SPIE, Visualization in Biomedical Computing, Vol. 1808,pp.169-213, 1992.
3. R. Kikinis, F.A. Jolesz, W.E. Lorensen, H.E. Cline, P.E Stieg, Black 3d Reconstruction of Skull Base Tumors from MRI Data for Neurosurgical Planning. In Proceedings of the Society of Magnetic Resonance in Medicine Conference, 1991.
4. R. Kikinis, D. Altobelli, W. Lorensen, W. Wells, and G. Ettinger. Pre- and intraoperative tumor localization using 3d renderings of mri's. 12th Annual Scientific Meeting of the Society of Magnetic Resonance in Medicine, 1993.
5. D. Levin, X. Hu, K. Tan, et al. The Brain: Integrated Three-Dimensional Display of MR and PET Images. Radiology, vol.172, pp.783-789 1989.
6. J. Belliveau, D. Kennedy, R. McKinstry, et al. Functional Mapping of the Human Visual Cortex by Magnetic Resonance Imaging. Science,vol.254 , pp, 716 -719, November 1991.
7. M. Vannier, D. Jordan, W. Murphy, Multi-Spectral Analysis of Magnetic Resonance Images. Radiology,vol. 154, pp.221-224, 1985.
8. M. Kohn, N. Tanna, G. Herman, et al. Analysis of Brain and Cerebrospinal Fluid Volumes with MR Imaging. Radiology,vol.178, pp. 115 -122, 1991.
9. Jain AK, Duin RP, Mao J. Statistical pattern recognition: A review. IEEE Trans PAMI 2000;22: 4-37.
10. Frigui H, Krishnapuram R. A robust competitive clustering algorithm with application in computer vision. IEEE Trans PAMI 1999;21:450-65.
11. Tseng LY, Yang SB. A genetic approach to the automatic clustering problem. Patten Recognition 2001;34:415-24.
12. Bandyopadhyay S. Simulated annealing using a reversible jump markov chain monte carlo algorithm for fuzzy clustering. IEEE Trans Knowledge Data Engg. 2005;17: 479-90.
13. Yang, M., Ahuja “Gaussian mixture model for human skin color and its application in image and video databases”, In Proc. of the SPIE: Conf. on Storage and Retrieval for Image and Video Databases (SPIE 99), Vol. 3656, 458–466.
14. K. Rose, E. Gurewitz, G. C. Fox, "Vector quantization by deterministic annealing," IEEE Trans. Information Theory, vol. 38, no. 4, pp. 1249-1257, July 1992.
15. G. Fung and O. L. Mangasarian “Semi-supervised support vector machines for unlabeled data classification”, Optimization Methods and Software, 15, pp. 29–44, 2001.
16. M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE transaction on Image Processing, vol. 4, No. 11, pp. 1549–1560, 1995.
17. B. Wang and L. Zhang, “Supervised texture segmentation using wavelet transform,” Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol. 2, pp. 1078–1082, 2003.
18. Chang-Tsun Li and Roland Wilson, “Unsupervised texture segmentation using multiresolution hybrid genetic algorithm,” Proc. IEEE International Conference on Image Processing ICIP03, pp. 1033–1036, 2003.
19. T. R. Reed and J. M. H. Du Buf, “A review of recent texture segmentation, feature extraction techniques,” in CVGIP Image Understanding, vol.57, pp. 359–372, 1993.
20. A. K. Jain and K. Karu, “Learning texture discrimination masks,” IEEE transactions of Pattern Analysis and Machine Intelligence, Vol. 18, No. 2, pp. 195–205, 1996.
21. Eldman de Oliveira Nunes and Aura Conci, “Texture segmentation considering multi band, multi resolution and affine invariant roughness,” SIBGRAPI, pp. 254–261,2003.
22. L.F. Eiterer, J. Facon, and D. Menoti, “Postal envelope address block location by fractal-based approach,” 17th Brazilian Symposium on Computer Graphics and Image Processing, D. Coppersmith, Ed., pp. 90–97,2004,
23. Robert M. Haralick , Statistical and Structural Approaches to Texture, IEEE Proceedings, vol. 67, issue.5,pp.786-804, May 1979.
24. Serge Beucher and Christian Lantuejoul, “Use of watersheds in contour detection”. International workshop on image processing, real-time edge and motion detection, 1979.
25. Leila Shafarenko and Maria Petrou, “Automatic Watershed Segmentation of Randomly Textured Color Images”, IEEE Transactions on Image Processing, vol.6,issue.11, pp.1530-1544, 1997.
26. Basim Alhadidi, Mohammad H. et al, “Mammogram Breast Cancer Edge Detection Using Image Processing Function” Information Technology Journal vol.6, issue.2, pp.217-221, ISSN-1812-5638,2007

Segmentation GLCM Watershed KMCG KFCG