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• W4313531885 endingPage "104511" @default.
• W4313531885 startingPage "104511" @default.
• W4313531885 abstract "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." @default.
• W4313531885 created "2023-01-06" @default.
• W4313531885 creator A5001284314 @default.
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• W4313531885 creator A5035499884 @default.
• W4313531885 creator A5056280508 @default.
• W4313531885 creator A5088159519 @default.
• W4313531885 date "2023-04-01" @default.
• W4313531885 modified "2023-10-15" @default.
• W4313531885 title "Image segmentation of Intracerebral hemorrhage patients based on enhanced hunger Games search Optimizer" @default.
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