Group theoretic particle swarm optimization for gray-level medical image enhancement

Jinyun Jiang; Jianchen Cai; Qile Zhang; Kun Lan; Xiaoliang Jiang; Jun Wu; Jinyun Jiang; Jianchen Cai; Qile Zhang; Kun Lan; Xiaoliang Jiang; Jun Wu

doi:10.3934/mbe.2023462

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10479-10494. doi: 10.3934/mbe.2023462

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Group theoretic particle swarm optimization for gray-level medical image enhancement

1.
College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
2.
Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China

Academic Editor: Simon James Fong
† The authors contributed equally to this work.

Received: 09 January 2023 Revised: 30 March 2023 Accepted: 03 April 2023 Published: 07 April 2023

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.
- medical image enhancement,
- metaheuristics,
- group theory,
- intensity transformation
Citation: Jinyun Jiang, Jianchen Cai, Qile Zhang, Kun Lan, Xiaoliang Jiang, Jun Wu. Group theoretic particle swarm optimization for gray-level medical image enhancement[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10479-10494. doi: 10.3934/mbe.2023462

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Abstract

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.

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