Application of Immune Clone Selection Algorithm to Image Segmentation

Image segmentation is a significant part in image processing field. Inspired by the threshold-based segmentation methods, a novel algorithm based on immune clone selection and optimal entropy theory is presented in this paper. Immune clone selection algorithm performs not only local but also global search, and has better performance than Genetic Algorithm(GA) in searching for the optimal entropy threshold of images. The algorithm is depicted in detail and the computational complexity is given. In experiments, natural image and SAR image are selected, and the algorithm runs ten times independently and the mean numbers of function values are presented as the evaluation of the algorithm complexity. It shows that the algorithm presented in this paper can find better solutions with small generation and mean numbers of function values. So this method has better performance in stabilization and convergence than GA. Experimental results show that this method is feasible and effective.