结合小波变换和改进邻域权值的FCM算法

针对改进的模糊C均值聚类算法在进行图像分割时构建的邻域权值函数未能同时考虑空间结构信息和灰度值域信息,而导致对噪声敏感及边缘纹理信息的处理粗糙的问题,提出了一种结合小波变换和改进邻域权值的FCM算法.该算法首先在原始灰度图像的基础上进行小波多分辨率分析的自适应阈值去噪处理;然后在重构图像上结合双边滤波的思想构建一个基于图像块局部空间邻域信息和灰度值域信息的改进邻域权值函数.实验结果表明,该算法比传统FCM算法以及FCM的改进算法有更高的分割精确度,对强噪声更具鲁棒性,图像边缘也更加平整.

FCM Algorithm Combined with Wavelet Transform and Improved Neighborhood Weights

The neighborhood weight function built in image segmentation using the improved fuzzy c-means clustering algorithm fails to simultaneously consider space structure and grayscale range information, which results in the problem of noise sensitivity and rough dealing with edge texture information. To this problem, in this paper, a FCM algorithm combined with wavelet transform and improved neighborhood weights is proposed. First, the algorithm deals with the original gray image by using the adapt threshold denoising method, which is based on wavelet used for multi-resolution analysis. Second, it constructs an improved neighborhood weight function based on the local spatial neighborhood information and grayscale range information of the image patches by combining with the thought of bilateral filtering in the reconstructed image. The experiment results show that the proposed algorithm has a higher accuracy of segmentation than the traditional FCM algorithm and improved FCM algorithm and is more robustness to the strong noise with more smooth image edges.