Threshold method is an important image segmentation method, which has been widely used in image segmentation. For this method, it is very important to choose a good threshold. The traditional threshold segmentation algorithm is implemented by exhaustive method, which makes the solution efficiency very low. This paper presents a collective neurodynamic optimization algorithm to solve the problem of binary optimization in the image segmentation. The problem of image segmentation based on threshold is transformed into binary optimization with constraints. Then, a collective neurodynamic optimization algorithm is introduced which combined with feedback neural network and particle swarm optimization (PSO) algorithm. And the linear programming relaxation constraint method is used to relax binary constraints. It is proved by numerical simulation that the feedback neural network algorithm can converge to the exact local optimal solution of the model and the PSO algorithm can get a better local optimal solution. Finally, several sets of comparative experiments are presented. The feasibility of our proposed method is verified; the experimental results demonstrate the effectiveness of our approach in image segmentation. In this study, a collective neurodynamic optimization was proposed for the image segmentation problem. In the future, we expect that multiple centralized neurodynamic models and intelligent algorithms can be used to solve the problem and improve the convergence speed of the solved model.

阈值法是一种重要的图像分割方法，已广泛应用于图像分割中。对于这种方法，选择一个好的阈值非常重要。传统的阈值分割算法采用穷举法实现，求解效率非常低。本文提出了一种集体神经动力学优化算法来解决图像分割中的二进制优化问题。基于阈值的图像分割问题被转化为具有约束的二进制优化。然后，提出了一种结合反馈神经网络和粒子群优化算法的集体神经动力学优化算法。然后使用线性规划松弛约束方法来松弛二进制约束。数值仿真表明，反馈神经网络算法可以收敛到模型的精确局部最优解，而PSO算法可以获得更好的局部最优解。最后，提出了几套比较实验。验证了所提方法的可行性。实验结果证明了我们的方法在图像分割中的有效性。在这项研究中，针对图像分割问题提出了一种集体神经动力学优化方法。未来，我们希望可以使用多个集中式神经动力学模型和智能算法来解决问题，并提高求解模型的收敛速度。