Improved Fuzzy C-Means Algorithm Based On Gray-Level For Image Segmentation

Fuzzy c-means algorithm based on gray-level is a fast image segmentation algorithm, which cannot effectively segment the object pixels and background pixels of the non-destructive testing (NDT) image with the characteristics of unbalanced gray distribution. Then an improved fuzzy C-means algorithm based on gray-level (IFCMG) is proposed. Firstly, the expression of total membership degree of each cluster is constructed by using pixel numbers and membership degrees of gray-level, and it is integrated into the objective function, which can equalize the contribution of the object pixels and background pixels to the objective function. Secondly, the new membership degree and cluster center are strictly deduced. And then, considering that the density of clusters also affects the clustering results, we design the formula of compactness and integrate it into the clustering process. Finally, the NDT images are used for segmentation experiment. For each image, IFCMG has higher index values of F_value when the images are disturbed by different noise levels. We comprehensively evaluate the values of F_value obtained above, and find that the comprehensively evaluation value of the proposed algorithm is 26.13%, 16.46%, 13.75% and 25.10% higher than those of the comparison algorithms, respectively. The proposed algorithm can effectively segment NDT images with unbalanced gray distribution, which expands the application scope of fuzzy C-means algorithm based on gray-level.