哈里斯鹰算法优化脉冲耦合神经网络的图像自动分割

Harris hawk algorithm for optimizing pulse coupled neural network for automatic image segmentation

为了降低脉冲耦合神经网络(pulse coupled neural network,PCNN)参数设置的复杂度,提出了一种利用哈里斯鹰优化算法(Harris Hawk optimization algorithm,HHO)搜索PCNN参数的图像自动分割方法。一方面,在不影响分割效果的情况下,减少了PCNN的参数个数;另一方面,HHO算法具有收敛速度快、全局搜索能力强的特点,能够快速、准确地搜索到PCNN相应参数。引入图像熵作为适应度函数,选取脑部MRI图像进行实验,通过精度、召回率和dice,比较了HHO结合PCNN与几种不同搜索机制的优化算法结合PCNN的分割性能,仿真实验结果表明,提出的方法有较高的分割精度和较强的鲁棒性,具有较高的工程实用价值。

In order to reduce the complexity of setting parameters of pulse coupled neural network(PCNN),an au?tomatic image segmentation method is proposed,which uses Harris Hawk optimization algorithm(HHO)to search PCNN parameters.On the one hand,without affecting the segmentation effect,the number of PCNN parameters is reduced.On the other hand,Harris Hawk optimization algorithm has the characteristics of fast convergence and strong global search ability,and can quickly and accurately search the corresponding parameters of PCNN.The im?age entropy is introduced as the fitness function,and brain MRI images are selected for experiment.The segmenta?tion performance of HHO combined with PCNN is compared with several optimization algorithms that combine PC?NN and have different search mechanisms in aspects of precision,recall and dice.The simulation results show that the proposed method has higher segmentation accuracy and robustness,and has higher engineering application value.