Abstract: Because fuzzy C-means algorithm（FCM） can not automatically determine the cluster centers, and doesn’t consider the neighborhood pixels’ information, a new automatic image segmentation method is proposed based on FCM and k-means.The cluster number of the image is obtained by image histogram.And the initial cluster centers are obtained by using an improved fast FCM method.That is, they are obtained by using one step [k]-means algorithm for large membership degree gray values and only updating the small membership values using fast FCM.It iterates to obtain the initial cluster centers.Image segmentation can be done by using improved membership FCM algorithm.Experiments show that this method generates a closer initial cluster center values to the final clustering centers, reduces the computing time, and has stronger anti-noise property.

Key words: [k]-means, fuzzy c-means, image segmentation, neighborhood information

摘要： 针对图像分割中模糊C均值算法（FCM）无法自动确定聚类中心，不考虑像素邻域信息的问题，提出一种结合[k]-means的自动FCM图像分割方法。该方法先由图像的灰度直方图确定聚类数目，使用一种改进的快速FCM方法产生初始聚类中心。即通过一步[k]-means算法对大隶属度灰度更新模糊聚类中心，同时仅对小隶属度灰度使用快速FCM?方法进行隶属度更新，迭代后得到初始聚类中心。利用改进隶属度的FCM算法进行最终聚类。实验表明，该方法获取初始聚类中心接近最终值，加速图像分割，并对噪声具有一定的鲁棒性。

关键词: [k]均值, 模糊C均值, 图像分割, 邻域信息