Abstract: This study presents a novel hybrid optimization model that combines the complementary aspects of Sand Cat Swarm Optimization (SCSO) and Whale Optimization Algorithm (WOA) to solve the multi-level image thresholding problem. The proposed approach utilizes an adaptive two-stage mechanism that balances the high exploration capacity of SCSO with the concentrated local search capability of WOA, aiming to maximize inter-class variance in the histogram-based thresholding process. Various experiments are conducted on lung cancer, prostate, and mixed medical image datasets. Results demonstrate that modified SCSOWOA achieves superior performance across all datasets. For LC25000, it attains PSNR 27.9453 dB, SSIM 0.9340, FSIM 0.9542, Dice coefficient 0.8901, and Jaccard index 0.8034 at T?=?12. For prostate, PSNR reaches 28.3965 dB, SSIM 0.7532, FSIM 0.8170, Dice 0.9215, and Jaccard 0.8593. In the MSD dataset, SCSOWOA achieves PSNR 29.3244 dB, SSIM 0.7118, FSIM 0.7562, Dice 0.8901, and Jaccard 0.8034, indicating consistent performance across diverse organs and modalities. The method also demonstrates high computational efficiency, with an average execution time of 1.3221 s, offering up to 40% speed improvement over conventional metaheuristics such as PSO and GWO. Overall, proposed method provides high-accuracy, low-variance, and computationally efficient segmentation, preserving both structural and perceptual fidelity. These results confirm the method’s robustness, generalizability, and practical applicability for AI-assisted diagnostic systems across histopathological and medical imaging contexts, balancing precision, structural preservation, and speed for real-world clinical deployment.

Key words: Hybrid meta-heuristics')">Hybrid meta-heuristics, Image processing, Multi-level image thresholding, Medical image segmentation