Abstract
•An enhanced Hunger Games Search algorithm (SCHGS) is proposed for cerebral hemorrhage images segmentation.•Slime mould position update mechanism and chaotic optimal solution variation are introduced.•The superior performance of the proposed method is verified by comparison with competitive peers.•An accurate SCHGS-based Kapur’s entropy multi-threshold image segmentation method is proposed.
Medical diseases seriously affect human life and health, and a segmentation model that can effectively support doctors in making the correct diagnoses of medical disease images is needed. Multi-threshold image segmentation is famous for its simplicity and ease of implementation, but the choice of its threshold combination affects its performance, and traditional optimization algorithms fall into local optimality with significant time consumption when solving such problems. Therefore, metaheuristic algorithms have been applied to this field, but many have drawbacks, such as slow convergence, easy prematureness, and unbalanced performance when performing threshold selection. For instance, the Hunger Games Search (HGS) algorithm proposed last year is unsatisfactory regarding convergence accuracy and speed. Hence, an improved HGS (SCHGS) is proposed by combining the slime mould position update mechanism and chaotic optimal solution variation. The slime position update mechanism has a powerful exploration capability, which can help HGS increase the exploration of the search space and find the optimal solution as much as possible. On the other hand, the chaotic optimal solution variation strengthens the algorithm's local exploitation ability, which can effectively avoid falling into the local optimum. The experimental results on benchmark test functions indicate that the convergence performance of SCHGS is improved by 54% compared with the original algorithm, and there is a more obvious advantage in the convergence speed. In the application of threshold selection in brain hemorrhage image segmentation, the performance of the suggested method also improves by 0.08%, 0.55%, and 0.29% according to different evaluation metrics (FSIM, PSNR, and SSIM), further demonstrating the effectiveness of SCHGS in solving image segmentation problems.