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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation
This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solut...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902839/ https://www.ncbi.nlm.nih.gov/pubmed/36777369 http://dx.doi.org/10.1007/s42235-023-00332-2 |
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author | Abualigah, Laith Habash, Mahmoud Hanandeh, Essam Said Hussein, Ahmad MohdAziz Shinwan, Mohammad Al Zitar, Raed Abu Jia, Heming |
author_facet | Abualigah, Laith Habash, Mahmoud Hanandeh, Essam Said Hussein, Ahmad MohdAziz Shinwan, Mohammad Al Zitar, Raed Abu Jia, Heming |
author_sort | Abualigah, Laith |
collection | PubMed |
description | This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature. |
format | Online Article Text |
id | pubmed-9902839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-99028392023-02-07 Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation Abualigah, Laith Habash, Mahmoud Hanandeh, Essam Said Hussein, Ahmad MohdAziz Shinwan, Mohammad Al Zitar, Raed Abu Jia, Heming J Bionic Eng Research Article This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature. Springer Nature Singapore 2023-02-07 /pmc/articles/PMC9902839/ /pubmed/36777369 http://dx.doi.org/10.1007/s42235-023-00332-2 Text en © Jilin University 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article Abualigah, Laith Habash, Mahmoud Hanandeh, Essam Said Hussein, Ahmad MohdAziz Shinwan, Mohammad Al Zitar, Raed Abu Jia, Heming Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation |
title | Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation |
title_full | Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation |
title_fullStr | Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation |
title_full_unstemmed | Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation |
title_short | Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation |
title_sort | improved reptile search algorithm by salp swarm algorithm for medical image segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902839/ https://www.ncbi.nlm.nih.gov/pubmed/36777369 http://dx.doi.org/10.1007/s42235-023-00332-2 |
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