Timely identification and treatment of rapidly progressing skin cancers can significantly contribute to the preservation of patients' health and well-being. Dermoscopy, a dependable and accessible tool, plays a pivotal role in the initial stages of skin cancer detection. Consequently, the effective processing of digital dermoscopy images holds significant importance in elevating the accuracy of skin cancer diagnoses. Multilevel thresholding is a key tool in medical imaging that extracts objects within the image to facilitate its analysis. In this paper, an enhanced version of the Mud Ring Algorithm hybridized with the Whale Optimization Algorithm, named WMRA, is proposed. The proposed approach utilizes bubble-net attack and mud ring strategy to overcome stagnation in local optima and obtain optimal thresholds. The experimental results show that WMRA is powerful against a cluster of recent methods in terms of fitness, Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).

翻译：及时识别和治疗快速进展的皮肤癌可显著促进患者健康与福祉的维护。皮肤镜作为一种可靠且易得的工具，在皮肤癌早期检测中发挥着关键作用。因此，数字化皮肤镜图像的有效处理对提升皮肤癌诊断准确性具有重要意义。多阈值分割是医学图像处理中的关键工具，通过提取图像中的目标对象以辅助分析。本文提出一种增强型泥环算法与鲸鱼优化算法混合的改进版本（命名为WMRA）。该方法利用气泡网攻击和泥环策略克服局部最优停滞问题，从而获得最优阈值。实验结果表明，在适应度、峰值信噪比（PSNR）和均方误差（MSE）指标上，WMRA相较于近期多种方法展现出显著优越性。