快速模糊C均值聚类的图像分割方法

Fast fuzzy C-Means clustering for image segmentation

模糊C均值(FCM)聚类算法广泛应用于图像的自动分割,但标准的FCM算法存在计算量大,运算速度慢等问题。对FCM算法进行改进,提出了一种快速FCM图像分割算法(FFCM),该算法将图像从像素空间映射到其灰度直方图特征空间,并在此基础上,充分利用像素的邻域特性,对隶属度函数做一定改进,实验结果表明该算法能快速有效地分割图像,并具有较好的抗噪能力。

FCM clustering algorithm is widely applied to automated image segmentation.But standard FCM algorithm has many problems,such as great amount of calculation and slow operation speed.This paper proposes a modified fast FCM algorithm for image segmentation.With the modified algorithm,images can be mapped to gray-scale histogram space from pixel space,on the basis of which membership function can be improved by the full use of pixel＇s neighborhood feature.The new algorithm is shown to be effective in image segmentation and has good performance of resisting noise.