Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images

摘要：

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.

作者: Andac Hamamci Gozde Unal Nadir Kucuk Kayihan Engin

作者单位: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey Department of Radiation Oncology, Anadolu Medical Center, Kocaeli, Turkey

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 137–146

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）