DCAO: Enhanced AO for Breast Cancer Histopathological Segmentation

Variations in tissue morphology and image quality make threshold selection challenging. Furthermore, traditional pathological diagnosis relies heavily on expert interpretation and is subject to subjective variability, which limits diagnostic accuracy and efficiency. Therefore, this study proposes an improved AO (DCAO) incorporating the random follower search (RFS) strategy and the crisscross optimization strategy (CC). The RFS enhances inter-population communication by reducing the algorithm’s reliance on the current global best individual. The CC improves population diversity, facilitates escaping local optima, and strengthens the algorithm’s global search capability. DCAO’s performance was validated in two parts. First, it was benchmarked against 24 algorithms on the IEEE CEC 2014 and 2022, where statistical analysis revealed its robust competitiveness. Second, when applied to breast cancer histopathological segmentation against eight algorithms, across all tested threshold levels, DCAO ranked first on FSIM, PSNR, and SSIM—improving them by 1.03%, 2.70%, and 0.66% over the second-best method, respectively. It also achieved the highest 2D Kapur entropy, the fastest convergence, and demonstrably superior visual quality with clearer boundaries. This method provides a reliable and efficient solution for cancer image analysis, offering technical support for accurate lesion localization and treatment planning. However, it has limitations: the introduction of strategies increases computational complexity; the algorithm is designed only for 2D images; and its robustness against staining variations has not been systematically evaluated.