To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing the survival of the fittest mechanism. Then, a product cross entropy is utilized as the fitness function of the ICSO algorithm, and the parameter values of the ISPCNN model are determined through the effective teamwork of roosters, hens, and chicks in the chicken swarm. Finally, we can achieve the automatic image segmentation via the ISPCNN model, which has the best parameter values. The detailed experiments indicate that our method has more superior performance in terms of convergence and segmentation accuracy than methods based on the genetic algorithm and ant colony optimization algorithm.

为了解决脉冲耦合神经网络（PCNN）中参数设置的问题，我们提出了一种基于改进的鸡群优化算法和改进的简化PCNN（ICSO-ISPCNN）模型的新图像分割方法。首先，我们通过修改动态阈值函数来改进简化的PCNN模型，同时通过引入适者生存机制来改进鸡群优化（CSO）算法。然后，乘积交叉熵被用作ICSO算法的适应度函数，并且通过鸡群中公鸡，母鸡和小鸡的有效协作来确定ISPCNN模型的参数值。最后，我们可以通过具有最佳参数值的ISPCNN模型来实现自动图像分割。