An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation
Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/8/1422 |
| _version_ | 1797605772763332608 |
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| author | Essam H. Houssein Gaber M. Mohamed Nagwan Abdel Samee Reem Alkanhel Ibrahim A. Ibrahim Yaser M. Wazery |
| author_facet | Essam H. Houssein Gaber M. Mohamed Nagwan Abdel Samee Reem Alkanhel Ibrahim A. Ibrahim Yaser M. Wazery |
| author_sort | Essam H. Houssein |
| collection | DOAJ |
| description | Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms. |
| first_indexed | 2024-03-11T05:05:48Z |
| format | Article |
| id | doaj.art-37f5d36d7ee44ba3bc1e609824db447b |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:05:48Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-37f5d36d7ee44ba3bc1e609824db447b2023-11-17T18:54:52ZengMDPI AGDiagnostics2075-44182023-04-01138142210.3390/diagnostics13081422An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image SegmentationEssam H. Houssein0Gaber M. Mohamed1Nagwan Abdel Samee2Reem Alkanhel3Ibrahim A. Ibrahim4Yaser M. Wazery5Faculty of Computers and Information, Minia University, Minia 61519, EgyptFaculty of Computers and Information, Minia University, Minia 61519, EgyptDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Computers and Information, Minia University, Minia 61519, EgyptFaculty of Computers and Information, Minia University, Minia 61519, EgyptImage segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.https://www.mdpi.com/2075-4418/13/8/1422search and rescue optimization algorithmmeta-heuristicsopposition-based learningmulti-level thresholdingfuzzy entropy and Otsu methodimage segmentation |
| spellingShingle | Essam H. Houssein Gaber M. Mohamed Nagwan Abdel Samee Reem Alkanhel Ibrahim A. Ibrahim Yaser M. Wazery An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation Diagnostics search and rescue optimization algorithm meta-heuristics opposition-based learning multi-level thresholding fuzzy entropy and Otsu method image segmentation |
| title | An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation |
| title_full | An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation |
| title_fullStr | An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation |
| title_full_unstemmed | An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation |
| title_short | An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation |
| title_sort | improved search and rescue algorithm for global optimization and blood cell image segmentation |
| topic | search and rescue optimization algorithm meta-heuristics opposition-based learning multi-level thresholding fuzzy entropy and Otsu method image segmentation |
| url | https://www.mdpi.com/2075-4418/13/8/1422 |
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