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.

Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India

Computer Science Department Al Al Bayt University Mafraq 25113 Jordan

Computer Technologies Engineering Mazaya University College Nasiriyah Iraq

Department of Computer Science and Engineering College of Applied Studies and Community Service King Saud University 11362 Riyadh Saudi Arabia

Department of Computer Science College of Science for Women University of Baghdad Baghdad Iraq

Department of Mathematics Facility of Science The Hashemite University P O box 330127 Zarqa 13133 Jordan

Faculty of Educational Sciences Al Ahliyya Amman University Amman 19328 Jordan

Faculty of Electrical Engineering and Computer Science VŠB Technical University of Ostrava 70800 Poruba Ostrava Czech Republic

Faculty of Information Technology Jadara University Irbid 21110 Jordan

Unit for Data Science and Computing North West University 11 Hofman Street Potchefstroom 2520 South Africa

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