一种融合邻域信息的模糊C-均值图像分割算法

Fuzzy C-means image segmentation algorithm incorporating neighborhood information

模糊C-均值算法(fuzzy C-means,FCM)对图像噪声敏感,只考虑了图像数值信息而忽略了邻域空间信息,造成最终的图像分割结果不精确。为了克服FCM存在的问题,将图像局部信息与非局部信息融入到多测度模型中,扩充了原本聚类的单一测度。另外将先验概率引入隶属度矩阵中,使得每次迭代前,隶属度矩阵中像素点的邻域信息都被充分考虑,最后添加一个邻域隶属度惩罚项修正聚类结果。实验证明:该算法对噪声鲁棒性强,能够获得较为理想的图像分割效果。

The fuzzy C-means algorithm(FCM)is sensitive to image noise;in addition,it only considers the image numerical information and ignores the neighborhood spatial information,resulting in inaccurate final image segmentation result.To overcome this drawback,an FCM image segmentation algorithm is proposed in which the local information and non-local information of the image are integrated into a multidimensional model,which extends the original single dimension of clustering.In addition,a prior probability is introduced into the membership matrix,so that the neighborhood information of the pixel in the membership matrix is fully considered before each iteration,and then a neighborhood membership penalty is added to correct the clustering result.Finally,a penalty of neighborhood membership degree is used to modify the clustering results.Experimental results demonstrate that the algorithm is robust against noise and achieves an ideal image segmentation effect.