International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 6 | Year 2013 | Article Id. IJCTT-V4I6P167 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I6P167

Medical Image Segmentation Using Modified K Means And Cellular Automata Algorithms

Sruthi K

Citation :

Sruthi K, "Medical Image Segmentation Using Modified K Means And Cellular Automata Algorithms," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 6, pp. 1875-1878, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I6P167

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.

Keywords

Brain tumor ,Segmentation, Medical image ,Cellular Automata, Modified K Means.

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