Group theoretic particle swarm optimization for gray-level medical image enhancement
As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. T...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-04-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023462?viewType=HTML |
| _version_ | 1797830129925226496 |
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| author | Jinyun Jiang Jianchen Cai Qile Zhang Kun Lan Xiaoliang Jiang Jun Wu |
| author_facet | Jinyun Jiang Jianchen Cai Qile Zhang Kun Lan Xiaoliang Jiang Jun Wu |
| author_sort | Jinyun Jiang |
| collection | DOAJ |
| description | As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process. |
| first_indexed | 2024-04-09T13:32:15Z |
| format | Article |
| id | doaj.art-d705202e5db7409292f47a664103e30b |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-09T13:32:15Z |
| publishDate | 2023-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-d705202e5db7409292f47a664103e30b2023-05-10T01:14:18ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-04-01206104791049410.3934/mbe.2023462Group theoretic particle swarm optimization for gray-level medical image enhancementJinyun Jiang0Jianchen Cai1Qile Zhang2Kun Lan 3Xiaoliang Jiang4Jun Wu51. College of Mechanical Engineering, Quzhou University, Quzhou 324000, China1. College of Mechanical Engineering, Quzhou University, Quzhou 324000, China2. Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China1. College of Mechanical Engineering, Quzhou University, Quzhou 324000, China1. College of Mechanical Engineering, Quzhou University, Quzhou 324000, China1. College of Mechanical Engineering, Quzhou University, Quzhou 324000, ChinaAs a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process.https://www.aimspress.com/article/doi/10.3934/mbe.2023462?viewType=HTMLmedical image enhancementmetaheuristicsgroup theoryintensity transformation |
| spellingShingle | Jinyun Jiang Jianchen Cai Qile Zhang Kun Lan Xiaoliang Jiang Jun Wu Group theoretic particle swarm optimization for gray-level medical image enhancement Mathematical Biosciences and Engineering medical image enhancement metaheuristics group theory intensity transformation |
| title | Group theoretic particle swarm optimization for gray-level medical image enhancement |
| title_full | Group theoretic particle swarm optimization for gray-level medical image enhancement |
| title_fullStr | Group theoretic particle swarm optimization for gray-level medical image enhancement |
| title_full_unstemmed | Group theoretic particle swarm optimization for gray-level medical image enhancement |
| title_short | Group theoretic particle swarm optimization for gray-level medical image enhancement |
| title_sort | group theoretic particle swarm optimization for gray level medical image enhancement |
| topic | medical image enhancement metaheuristics group theory intensity transformation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023462?viewType=HTML |
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