Cellular automata segmentation of brain tumors on post contrast MR images

Authors:
Andac Hamamci;Gozde Unal;Nadir Kucuk;Kayihan Engin
Affiliations:
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey;Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey;Department of Radiation Oncology, Anadolu Medical Center, Kocaeli, Turkey;Department of Radiation Oncology, Anadolu Medical Center, Kocaeli, Turkey
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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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.