肺癌是一个严重的健康问题，需要快速、准确的诊断才能进行有效的治疗。在医学成像中，分割对于识别和隔离感兴趣区域至关重要，这对于精确诊断和治疗计划至关重要。传统的基于元启发式的分割方法经常遇到收敛速度慢、优化阈值结果差、平衡探索和利用等问题，导致肺癌图像的多阈值分割性能不佳。本研究提出了 ASG-HMO，它是人类记忆优化 (HMO) 算法的增强变体，因其简单性、多功能性和最小参数而被选中。尽管HMO从未应用于多阈值图像分割，但其特性使其非常适合改进病理肺癌图像分割。 ASG-HMO 纳入了四项创新策略，以解决细分过程中的关键挑战。首先，提出增强的自适应互利共生阶段来平衡探索和利用，以准确地描绘肿瘤边界，而不会陷入次优解决方案。其次，利用螺旋运动策略通过关注整体肺部结构和复杂的肿瘤细节来自适应地细化分割解决方案。第三，高斯变异策略在搜索过程中引入了多样性，使得能够探索更广泛的分割阈值，从而提高分割区域的准确性。最后，提出了自适应t分布扰动策略，帮助算法避免局部最优并在后期细化分割。 ASG-HMO 的有效性通过 IEEE CEC'17 和 CEC'20 基准套件的严格测试得到验证，随后将其应用于九个组织病理学肺癌图像的多级阈值分割。在这些实验中，测试了六种不同的分割阈值，并将该算法与几种经典的、最新的和先进的分割算法进行了比较。此外，所提出的 ASG-HMO 利用 2D Renyi 熵和 2D 直方图来提高分割过程的精度。病理肺癌分割的定量结果分析表明，ASG-HMO 实现了 31.924 的卓越最大峰值信噪比（PSNR）、0.919 的结构相似性指数测量（SSIM）、0.990 的特征相似性指数测量（FSIM）和概率兰德指数 (PRI) 为 0.924。这些结果表明 ASG-HMO 在收敛速度和分割精度方面都显着优于现有算法。这证明了 ASG-HMO 作为病理肺癌图像精确分割框架的稳健性，为改善临床诊断流程提供了巨大潜力。版权所有 © 2024 Elsevier Ltd。保留所有权利。

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation. The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages. The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process. Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.Copyright © 2024 Elsevier Ltd. All rights reserved.