Abualigah Laith, Al-Okbi Nada Khalil, Alomari Saleh Ali, Almomani Mohammad H, Moneam Sahar, Yousif Maryam A, Snasel Vaclav, Saleem Kashif, Smerat Aseel, Ezugwu Absalom E
Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan.
Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq.
Sci Rep. 2025 Apr 13;15(1):12713. doi: 10.1038/s41598-025-96429-1.
Image segmentation using bi-level thresholds works well for straightforward scenarios; however, dealing with complex images that contain multiple objects or colors presents considerable computational difficulties. Multi-level thresholding is crucial for these situations, but it also introduces a challenging optimization problem. This paper presents an improved Reptile Search Algorithm (RSA) that includes a Gbest operator to enhance its performance. The proposed method determines optimal threshold values for both grayscale and color images, utilizing entropy-based objective functions derived from the Otsu and Kapur techniques. Experiments were carried out on 16 benchmark images, which included COVID-19 scans along with standard color and grayscale images. A thorough evaluation was conducted using metrics such as the fitness function, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the Friedman ranking test. The results indicate that the proposed algorithm seems to surpass existing state-of-the-art methods, demonstrating its effectiveness and robustness in multi-level thresholding tasks.
使用双阈值的图像分割在简单场景中效果良好；然而，处理包含多个物体或颜色的复杂图像会带来相当大的计算困难。多级阈值处理对于这些情况至关重要，但它也引入了一个具有挑战性的优化问题。本文提出了一种改进的爬行动物搜索算法（RSA），该算法包括一个全局最佳算子以提高其性能。所提出的方法利用从大津法和卡普尔技术导出的基于熵的目标函数，为灰度图像和彩色图像确定最佳阈值。对16幅基准图像进行了实验，其中包括新冠病毒扫描图像以及标准彩色和灰度图像。使用适应度函数、峰值信噪比（PSNR）、结构相似性指数测量（SSIM）和弗里德曼排名检验等指标进行了全面评估。结果表明，所提出的算法似乎超越了现有的最先进方法，证明了其在多级阈值处理任务中的有效性和鲁棒性。
