中文摘要：

针对模糊局部信息C-均值（fuzzy local information C-means,FLICM）聚类算法因其局部空间信息的局限性而导致图像分割结果存在误差的问题,改进FLICM算法的相似度测量因子,并考虑邻域空间距离、灰度信息以及灰度方差对分割效果的影响,提出一种用于图像分割的模糊局部信息C均值的修正算法（WFLICM）.实验结果表明,WFLICM能够估算邻域像素的衰减程度,提高图像的分割性能,在抑制噪声的同时更好地保留图像细节,且具有更好的抗噪鲁棒性.

英文摘要：

FLICM（fuzzy local information C-means）fails to resolve the misclassification problem due to the limitation of local spatial information.In order to solve this problem,a modified FLICM is proposed for image segmentation,which improves the similarity measurement factor by taking into account the effects of spatial distance information,gray level and variance of gray level of neighborhood pixels.The modified algorithm（WFLICM）can accurately estimate the damping extent of neighboring pixels and can suppress noise at large scale while preserving more image details.Experimental results show that the algorithm can improve the performance of image segmentation and has better robustness to noise.