Medical Image Segmentation Using Modified K Means And Cellular Automata Algorithms
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - June Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-6 | ||
| Year of Publication : 2013 | ||
| Authors :Sruthi K |
Sruthi K "Medical Image Segmentation Using Modified K Means And Cellular Automata Algorithms "International Journal of Computer Trends and Technology (IJCTT),V4(6):1872-1878 June Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - Segmentation is widely used in medical industry to get abnormal growth data from the medical image like MRI and CT .In this paper, I present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction .K Means based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, is proposed. And the result is compared against Cellular automata based tumor segmentation method. Seed points are selected as the intersection of maximum white points row wise and column wise . First the seed pixels of tumor and background are fed to the algorithm. Using this seeds, the algorithm finds the strength maps for both tumor and background image .This maps are then combined to get the tumor probability map. Comparison studies on both clinical and synthetic brain tumor datasets for both this methods demonstrate performance of the proposed algorithm( K Means)in terms of, its efficiency and accuracy.
References-
[1] S. Warfield, K. Zou, and W. Wells, “Simultaneous truth and perfor-mance level estimation (STAPLE): An algorithm for the validation of image segmentation,” IEEE Trans. Med Imag., vol. 23, no. 7, pp. 903–921, Jul. 2004.
[2] M.-R. Nazem-Zadeh, E. Davoodi-Bojd, and H. Soltanian-Zadeh, “Atlasbased fiber bundle segmentation using principal diffusion directions and spherical harmonic coefficients,” NeuroImage, vol. 54, pp. S146–S164, 2011.
[3] K. H. Zou, S. K. Warfield, A. Bharatha, C. M. C. Tempany, M. R. Kaus, S. J. Haker, W. M. Wells, F. A. Jolesz, and R. Kikinis, “Statistical vali-dation of image segmentation quality based on a spatial overlap index,” Acad. Radiol., vol. 11, no. 2, pp. 178–189, 2004.
[4] E. D. Angelini, O. Clatz, E. Mandonnet, E. Konukoglu, L. Capelle, and H. Duffau, “Glioma dynamics and computational models: A re-view of segmentation, registration, and in silico growth algorithms and their clinical applications,” Curr. Med. Imag. Rev., vol. 3, no. 4, pp. 262–276, 2007.
[5] M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, “A brain tumor segmen-tation framework based on outlier detection,” Med. Image Anal., vol. 8, no. 3, pp. 275–283, 2004.
[6] J. Liu, J. K. Udupa, D. Odhner, D. Hackney, and G. Moonis, “A system for brain tumor volume estimation via MR imaging and fuzzy connect-edness,” Comput. Med. Imag. Graph., vol. 29, pp. 21–34, 2005.
[7] T. Biswaset al., “Stereotactic radiosurgery for glioblastoma: Retro-spectiveanalysis.,” Radiation Oncology, vol. 4, no. 11, p. 11, 2009. .
Keywords : Brain tumor ,Segmentation, Medical image ,Cellular Automata, Modified K Means.