Image Segmentation Based on Maximum Fuzzy Entropy and Clone Selection Algorithm

A threshold segmentation method based on maximum fuzzy entropy and immune clone selection algorithm is proposed to segment an image with nonuniform illumination.According to the maximum fuzzy entropy principle,the optimal combination of the fuzzy parameters is searched,and the optimal threshold is determined to distinguish the object and background.In order to validate the proposed method,it is tested and compared with genetic algorithm.Experimental results show that the proposed method gives better performance,and can select the threshold automatically and efficiently, and has an advantage of reservation of the main features of the original image.