The FLICM is an effective algorithm in image segmentation based on the FCM clustering framework. However, it is hard to obtain accurate results for dealing with strong noise-degraded images. By employing the local prior probability of the MRF model, the FLICM algorithm can be improved in two aspects. Firstly, the prior probability is used to weight the dissimilarity function when calculating the fuzzy factor. The refined fuzzy factor takes into account larger scale of neighboring constraints, which makes our algorithm more robust to noise. Secondly, the membership function is further weighted by the prior probability in the process of label determination. Because the neighboring labels must be taken into account in this process, our algorithm can obtain more homogeneous segmentation results. Compared with some other FCM-based algorithms, the proposed algorithm is applied to both synthetic images and real images to demonstrate its strong robustness.%FLICM算法是一种基于FCM框架的有效的分割方法.然而,它对于强噪声图像的分割仍然不够准确.本文使用MRF模型的局部先验概率,对FLICM算法从两方面进行了改进.首先,在计算模糊因子时,使用先验概率对距离函数进行加权.改进的模糊因子考虑了更大范围的邻域约束,从而使算法受噪声的影响程度减弱.其次,在分割阶段,进一步使用局部先验概率对FLICM算法的隶属度进行加权.使用改进后的隶属度进行标记判决,使得每一标记的确定需要考虑邻域标记的影响,使分割结果的区域性更好.利用新算法对模拟影像和真实影像进行了分割实验,并与几个考虑空间信息约束的FCM分割算法进行了对比分析,结果证明该算法具有更强的抗噪性能.
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