Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding
Abstract 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 introdu...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96429-1 |
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| author | Laith Abualigah Nada Khalil Al-Okbi Saleh Ali Alomari Mohammad H. Almomani Sahar Moneam Maryam A. Yousif Vaclav Snasel Kashif Saleem Aseel Smerat Absalom E. Ezugwu |
| author_facet | Laith Abualigah Nada Khalil Al-Okbi Saleh Ali Alomari Mohammad H. Almomani Sahar Moneam Maryam A. Yousif Vaclav Snasel Kashif Saleem Aseel Smerat Absalom E. Ezugwu |
| author_sort | Laith Abualigah |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-0c10c436e1ef4c2da2e1bb314e797c02 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0c10c436e1ef4c2da2e1bb314e797c022025-08-20T02:16:06ZengNature PortfolioScientific Reports2045-23222025-04-0115114510.1038/s41598-025-96429-1Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholdingLaith Abualigah0Nada Khalil Al-Okbi1Saleh Ali Alomari2Mohammad H. Almomani3Sahar Moneam4Maryam A. Yousif5Vaclav Snasel6Kashif Saleem7Aseel Smerat8Absalom E. Ezugwu9Computer Science Department, Al Al-Bayt UniversityDepartment of Computer Science, College of Science for Women, University of BaghdadFaculty of Information Technology, Jadara University Department of Mathematics, Facility of Science, The Hashemite UniversityDepartment of Computer Science, College of Science for Women, University of BaghdadDepartment of Computer Science, College of Science for Women, University of BaghdadFaculty of Electrical Engineering and Computer Science, VŠB-Technical University of OstravaDepartment of Computer Science & Engineering, College of Applied Studies & Community Service, King Saud UniversityFaculty of Educational Sciences, Al-Ahliyya Amman UniversityUnit for Data Science and Computing, North-West UniversityAbstract 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.https://doi.org/10.1038/s41598-025-96429-1Medical imagesImage segmentationMulti-level thresholdReptile search algorithmOtsu method, Kapur method |
| spellingShingle | Laith Abualigah Nada Khalil Al-Okbi Saleh Ali Alomari Mohammad H. Almomani Sahar Moneam Maryam A. Yousif Vaclav Snasel Kashif Saleem Aseel Smerat Absalom E. Ezugwu Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding Scientific Reports Medical images Image segmentation Multi-level threshold Reptile search algorithm Otsu method, Kapur method |
| title | Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding |
| title_full | Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding |
| title_fullStr | Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding |
| title_full_unstemmed | Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding |
| title_short | Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding |
| title_sort | optimized image segmentation using an improved reptile search algorithm with gbest operator for multi level thresholding |
| topic | Medical images Image segmentation Multi-level threshold Reptile search algorithm Otsu method, Kapur method |
| url | https://doi.org/10.1038/s41598-025-96429-1 |
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