The optimal segmentation of medical images remains important for promoting the intensive use of automatic approaches in decision making, disease diagnosis, and facilitating the sustainable development of computer vision studies. Generally, recent methods tend to minimize human–machine interaction by using multi-agent systems (MAS) and optimize the segmentation systems control. Some of the existing segmentation methods consider MAS qualifications and advantages but underline a lack of global optimization goals, and therefore they provide unsatisfactory results taking into account the need for precision in medical imaging. Our work coupled an improved MAS control protocol for medical image segmentation with the particle swarm optimization algorithm to strengthen the system for better result performance. The proposed method could relieve agents’ conflicts during the medical image segmentation for optimum control, better decision-making, and higher processing quality under the critical medical restrictions.

医学图像的最佳分割对于促进在决策，疾病诊断中促进自动方法的大量使用以及促进计算机视觉研究的可持续发展仍然很重要。通常，最近的方法倾向于通过使用多主体系统（MAS）来最大程度地减少人机交互，并优化分段系统的控制。一些现有的分割方法考虑了MAS的资格和优势，但强调了缺乏全局优化目标，因此考虑到医学成像精度的需求，它们提供的结果不令人满意。我们的工作将改进的用于医学图像分割的MAS控制协议与粒子群优化算法相结合，以增强系统的性能，从而获得更好的结果。