Image segmentation using adaptive immune genetic algorithm

In order to automatically segment a gray-scale image, this paper presents an adaptive immune genetic algorithm based on the cost minimization technique for image segmentation. The image segmentation problem is firstly cast as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is used. Then, the immune genetic algorithm is treated as an optimization technique to find the optimal solution. The presented algorithm recommends the usage of adaptive probabilities of crossover, mutation and immune operation. Furthermore, it effectively exploits some prior knowledge of pending problem and the information of evolved individual past history to make vaccines. Experimental results show that the algorithm performs well in terms of quality of the final segmented image and robustness to noise.