图像分割在医学图像分析中起着至关重要的作用，特别是在准确分离肿瘤和病变方面。有效的分割可以提高诊断精度并促进定量分析，这对于医疗专业人员至关重要。然而，由于相关的计算复杂性，传统的分割方法常常难以处理多级阈值。因此，确定最佳阈值集是一个 NP 难题，凸显了迫切需要有效的优化策略来克服这些挑战。本文介绍了一种多阈值图像分割 (MTIS) 方法，该方法集成了差分进化 (DE) 和小龙虾优化算法 (COA) 的混合方法，称为 HADECO。该方法利用二维（2D）卡普尔熵和2D直方图，旨在提高后续图像分析和诊断的效率和准确性。 HADECO 是一种混合算法，通过基于预定义规则交换信息来结合 DE 和 COA，利用两者的优势来获得卓越的优化结果。它采用拉丁超立方采样（LHS）来生成高质量的初始群体。 HADECO引入了改进的DE算法（IDE），对关键DE参数进行自适应和动态调整，并采用新的突变策略来增强其搜索能力。此外，它还结合了自适应COA（ACOA），可动态调整切换概率参数，有效平衡探索和利用。为了评估 HADECO 的有效性，首先使用 CEC'22 基准函数评估其性能。使用 Wilcoxon 符号秩检验 (WSRT) 和 Friedman 检验 (FT) 针对多种当代算法对 HADECO 进行评估，以整合结果。调查结果突显了 HADECO 卓越的优化能力，其弗里德曼平均排名最低 1.08 就证明了这一点。此外，还使用膝关节 MRI 图像和颅内出血 (ICH) CT 扫描对基于 HADECO 的 MTIS 方法进行了评估。脑出血图像分割的定量结果表明，该方法在6级阈值下实现了优异的平均峰值信噪比（PSNR）和特征相似性指数（FSIM）分别为1.5和1.7。在膝盖图像分割中，它在 5 级阈值下达到了 1.3 和 1.2 的平均 PSNR 和 FSIM，证明了该方法在解决图像分割问题方面的有效性。版权所有 © 2024 Elsevier Ltd. 保留所有权利。

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur's entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC'22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO's superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method's effectiveness in solving image segmentation problems.Copyright © 2024 Elsevier Ltd. All rights reserved.