ObjectiveThe integration of fuzzy entropy into image segmentation techniques has proven effective in addressing issues related to ambiguous boundaries and uncertainties in complex image structures. This study aims to improve breast mammography image segmentation by optimizing minimum fuzzy entropy. The primary challenges identified are the presence of glandular burrs and irregular edges, as well as premature convergence and local optima in entropy optimization. MethodsWe propose an enhanced differential evolution algorithm combined with fuzzy entropy to address these challenges. This algorithm specifically targets glandular burrs, irregular edges, premature convergence, and local optimal entropy issues. The algorithm's performance was evaluated using breast mammography images representing four types of glands and benchmarked against five state-of-the-art algorithms. ResultsExperimental results demonstrate that the proposed algorithm significantly improves segmentation accuracy compared to the other advanced algorithms. It effectively resolves issues related to glandular burrs, irregular edges, premature convergence, and local optimal entropy, leading to superior segmentation outcomes. ConclusionThe proposed algorithm, which combines an improved differential evolution approach with fuzzy entropy, delivers the best segmentation results for breast gland segmentation. It successfully overcomes the challenges of glandular burrs, irregular edges, premature convergence, and local optimal entropy, thus enhancing breast mammography image segmentation.