Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images
- Cite this paper as:
- Hamamci A., Unal G., Kucuk N., Engin K. (2010) Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images. In: Jiang T., Navab N., Pluim J.P.W., Viergever M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg
In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.
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