| Sumario: | Simple Summary: The precise segmentation of histopathological images, coupled with the preservation of cellular morphology, is of critical importance for the diagnosis and staging of colon cancer. However, conventional algorithms employed in the analysis of complex tissue architectures frequently suffer from premature convergence, becoming trapped in local optima as the dimensionality of the search space increases. To mitigate these limitations, a novel, label-independent hybrid algorithm characterized by highly efficient optimization stability is proposed. The primary objective of this study is to delineate cellular density boundaries by systematically integrating global exploration capabilities across broad search spaces with localized exploitation capacities. The derived findings demonstrate that the proposed methodology yields high structural accuracy without compromising the intrinsic tissue hierarchy. The outcomes of this research are anticipated to provide a robust diagnostic infrastructure for clinical decision support systems, free from topological deformations, thereby circumventing the reliance on extensively annotated datasets. Background/Objectives: Accurate segmentation of histopathological images while preserving cellular morphology in computer-aided diagnostic systems is critically important for the diagnosis and staging of colon cancer. However, conventional metaheuristic algorithms performing multi-level thresholding on such complex tissues often suffer from premature convergence by becoming trapped in local optima as the search space increases. To address this limitation, this study proposes a new label-independent hybrid optimization algorithm focused on colon adenocarcinoma segmentation. Methods: The proposed algorithm hybridizes the global exploration capability of the Artificial Rabbit Optimization (ARO) algorithm with the local exploitation ability of the Ant Lion Optimization (ALO) algorithm through an optimized fixed transition ratio, thereby enabling efficient localization of cellular density valleys. Results: The principal findings obtained from the LC25000 colon cancer dataset demonstrate that the ARO-ALO algorithm achieves stable performance with high SSIM (0.8043) and FSIM (0.8595) scores while preserving the histopathological hierarchy. Furthermore, the preservation of diagnostic morphology after segmentation is statistically validated by the high Pearson (0.9870) and Spearman (0.9948) correlation coefficients. In addition, supplementary generalization experiments are conducted on the Oral Squamous Cell Carcinoma (OSCC) and pulmonary circulation vessels datasets to verify the tissue-agnostic nature of the algorithm. Conclusions: Consequently, the ARO-ALO algorithm emerges as an efficient alternative for clinical decision support systems.
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