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.

• Klíčová slova
• Image segmentation, Medical images, Multi-level threshold, Otsu method, Kapur method, Reptile search algorithm,
• MeSH
• algoritmy * MeSH
• COVID-19 * diagnostické zobrazování   virologie   MeSH
• lidé MeSH
• počítačové zpracování obrazu * metody   MeSH
• poměr signál - šum MeSH
• SARS-CoV-2 izolace a purifikace   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH