Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.

中文翻译：

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一种改进的肺癌图像分割RIME优化算法

肺癌是世界范围内一种常见的癌症，需要早期准确的诊断才能成功治疗。在医学成像处理中，图像分割在医学诊断中起着至关重要的作用。本研究应用群体智能算法对肺癌病理图像进行三个层次的分割。原始算法结合了鲸鱼的搜索猎物机制和随机变异策略，产生了名为 WDRIME 的改进版本，旨在提高收敛速度并避免局部最优（LO）。此外，该研究还介绍了基于改进算法的肺癌多级图像分割方法。通过与 IEEE CEC2014 中最先进的算法进行比较，展示了 WDRIME 的性能。为了设计肺癌图像分割框架，本文将WDRIME算法与多级分割方法相结合。分割结果的评估采用 PSNR、SSIM 和 FSIM 等指标。总体而言，分析证实所提出的算法在收敛速度和准确性方面优于其他算法。该模型代表了一种高质量的分割方法，为肺癌病理图像的深入探索提供了实践支持。