Abstract
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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Keywords
- Cellular Automaton
- Cellular Automaton
- Short Path Algorithm
- Tumor Segmentation
- Image Segmentation Problem
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References
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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. https://doi.org/10.1007/978-3-642-15711-0_18
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DOI: https://doi.org/10.1007/978-3-642-15711-0_18
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