基于自适应加权图像块的广义模糊C均值算法

Generalized Fuzzy C-Means for Image Segmentation Based on Adaptive Weighted Image Patch

广义模糊C均值算法是一种比模糊C均值算法收敛速度更快的算法,然而它在分割灰度图像时对噪声敏感。为了改善其鲁棒性,提出基于图像块的像素灰度值加权的广义模糊C均值算法。该算法利用图像块代替单个像素构建目标函数,图像块内各像素的权重由邻域像素和中心像素空间关系及图像块内各像素灰度关系综合确定。以新目标函数为基础,利用拉格朗日乘子法推导出含图像块形式的隶属度和聚类中心表达式。通过这种方式,将邻域信息融入进聚类进程,提升算法的鲁棒性。利用合成图像和实际图像进行分割实验,结果表明:所提算法具有较强的鲁棒性和良好的分割性能。

Generalized fuzzy C-means algorithm is a faster convergence algorithm than fuzzy C-means algorithm.However,it is sensitive to noise when segmenting gray images.In order to improve its robustness,a generalized fuzzy C-means algorithm based on the weighting of pixel gray value in image patch is proposed.In this algorithm,instead of a single pixel,the image patch is used to construct the objective function.The weight of each pixel in the image patch is determined by the spatial relationship between neighboring pixels and central pixel and the gray relationship of each pixel in the image patch.The expressions of membership and cluster center,in the form of image patch,are derived by using Lagrange multiplier method based on the new objective function.In this way,the neighborhood information is integrated into the clustering process,and then improves the robustness of the algorithm.Segmentation experiments are carried out with a synthetic image and several real images,and the segmentation results show that the proposed algorithm has strong robustness and good segmentation performance.