一种基于FCM和LevelSet的MRI医学图像分割方法

FCM and Level Set Based Segmentation Method for Brain MR Images

对基于区域的几何活动轮廓模型中的区域项进行了改进.改进后的算法不仅解决了原算法存在的稳定性问题,同时也有效地提高了分割速度.算法还被成功地扩展到能够分割多种脑组织,且分割质量显著提高.多种子初始化方式的采用,进一步降低了算法对初始条件的敏感性,同时也减少了人工干预.对模拟和真实脑MRI图像的分割实验证明了改进的可行性和有效性,噪声条件下分割的比较分析也表明改进后的算法具有良好的韧性.

This paper improves the regional term of the region-based geometric active contour model initially proposed by J. S. Suri. Thanks to the new region based regularity term, the improved algorithm not only solves the underlying problem on the stability of the primary algorithm,but also effectively improves the speed of segmentation.Along with the more accurate segmentation performances,the algorithm is also able to segment various cerebral tissues such as the white matter, gray matter and cerebrospinal fluid. The random multi-seed initialization is used to further minimize the sensitivity of the algorithm to the initial condition while disusing the manual intervention. The experiments on simulative and real MR images demonstrate the feasibifity and the effectiveness of the improvement on the regional term. The comparison and analysis of the segmentation results under the noisy conditions also in- dicates the robustness of the proposed algorithm.