Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

图像分割是必不可少的预处理步骤，并且是图像分析必不可少的部分。本文提出了一种基于改进的Slime Mold算法（DASMA）的人一熵多阈值图像分割方法。首先，我们将扩散机制（DM）引入原始SMA中，以增加种群的多样性，从而使变体可以更好地避免陷入局部最优状态。然后添加关联策略（AS），以帮助算法更快地找到最佳解决方案。最后，将所提出的算法应用于基于非局部均值2D直方图的Renyi的熵多阈值图像分割。通过与一些知名算法进行比较，在伯克利细分数据集和基准（BSD）上证明了该方法的有效性。基于DASMA的多级阈值分割技术也已成功应用于慢性阻塞性肺疾病（COPD）的CT图像分割。实验结果通过图像质量指标进行评估，表明所提算法具有非凡的性能。这意味着它可以帮助医生定性和定量地分析病变组织，提高其诊断准确性并制定正确的治疗计划。有关本文的补充材料和信息，请访问https://aliasgharheidari.com。这意味着它可以帮助医生定性和定量地分析病变组织，提高其诊断准确性并制定正确的治疗计划。有关本文的补充材料和信息，请访问https://aliasgharheidari.com。这意味着它可以帮助医生定性和定量地分析病变组织，提高其诊断准确性并制定正确的治疗计划。有关本文的补充材料和信息，请访问https://aliasgharheidari.com。