Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images

• DOI: 10.1007/978-3-642-15711-0_18
• Publication Date: 2010-09-20T06:11:02Z
• Authors: Andac Hamamci; Gozde Unal; Nadir Kucuk; Kayihan Engin
• Keywords: Initialization; Smoothness

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.