Image Segmentation with PCNN Model and Immune Algorithm

In the domain of image processing, PCNN (Pulse Coupled Neural Network) need to adjust parameters time after time to obtain the better performance. To this end, we propose a novel PCNN parameters automatic decision algorithm based on immune algorithm. The proposed method transforms PCNN parameters setting problem into parameters optimization based on immune algorithm. It takes image entropy as the evaluation basis of the best fitness of immune algorithm so that PCNN parameters can be adjusted adaptively. Meanwhile, in order to break the condition that population information fall into local optimum, the proposed method introduces gradient information to affect the evolution of antibody to keep the population activity. Experiment results show that the proposed method realizes the adaptive adjustment of PCNN parameters and yields the better segmentation performance than many existing methods.