An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems
In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to s...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682400142X |
| _version_ | 1797224358231408640 |
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| author | Fatma A. Hashim Abdelazim G. Hussien Anas Bouaouda Nagwan Abdel Samee Ruba Abu Khurma Hayam Alamro Mohammed Azmi Al-Betar |
| author_facet | Fatma A. Hashim Abdelazim G. Hussien Anas Bouaouda Nagwan Abdel Samee Ruba Abu Khurma Hayam Alamro Mohammed Azmi Al-Betar |
| author_sort | Fatma A. Hashim |
| collection | DOAJ |
| description | In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels. |
| first_indexed | 2024-04-24T13:51:51Z |
| format | Article |
| id | doaj.art-b533fbc5ae8e4440a9c13001a2aeb37c |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-24T13:51:51Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-b533fbc5ae8e4440a9c13001a2aeb37c2024-04-04T05:03:19ZengElsevierAlexandria Engineering Journal1110-01682024-04-0193142188An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problemsFatma A. Hashim0Abdelazim G. Hussien1Anas Bouaouda2Nagwan Abdel Samee3Ruba Abu Khurma4Hayam Alamro5Mohammed Azmi Al-Betar6Faculty of Engineering, Helwan University, Egypt; MEU Research Unit, Middle East University, Amman 11831, JordanDepartment of Computer and Information Science, Linköping University, Linköping, Sweden; Faculty of Science, Fayoum University, Egypt; Applied Science Research Center, Applied Science Private University, Amman 11931, JordanFaculty of Science and Technology, Hassan II University of Casablanca, MoroccoDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaComputer Science Department, Faculty of Information Technology, Al-Ahliyya University, Amman, JordanDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Engineering and Information Technology, Ajman University, P.O. Box: 346, Ajman, United Arab Emirates; Artificial Intelligence Research Centre, Ajman University, P.O. Box: 346, Ajman, United Arab Emirates; Corresponding author at: Department of Information Technology, College of Engineering and Information Technology, Ajman University, P.O. Box: 346, Ajman, United Arab Emirates.In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels.http://www.sciencedirect.com/science/article/pii/S111001682400142XExponential distribution optimizerMulti-level thresholdingMeta-heuristic algorithmsImage segmentationOtsu's methodGlobal optimization |
| spellingShingle | Fatma A. Hashim Abdelazim G. Hussien Anas Bouaouda Nagwan Abdel Samee Ruba Abu Khurma Hayam Alamro Mohammed Azmi Al-Betar An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems Alexandria Engineering Journal Exponential distribution optimizer Multi-level thresholding Meta-heuristic algorithms Image segmentation Otsu's method Global optimization |
| title | An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems |
| title_full | An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems |
| title_fullStr | An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems |
| title_full_unstemmed | An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems |
| title_short | An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems |
| title_sort | enhanced exponential distribution optimizer and its application for multi level medical image thresholding problems |
| topic | Exponential distribution optimizer Multi-level thresholding Meta-heuristic algorithms Image segmentation Otsu's method Global optimization |
| url | http://www.sciencedirect.com/science/article/pii/S111001682400142X |
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