A multi-level thresholding image segmentation algorithm based on equilibrium optimizer

Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equili...

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Published in:Scientific reports Vol. 14; no. 1; pp. 29728 - 16
Main Authors: Hu, Pei, Han, Yibo, Zhang, Zheng, Chu, Shu-Chuan, Pan, Jeng-Shyang
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 29.11.2024
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
639/705/117
Algorithms
Brain research
Computer vision
Entropy
Equilibrium
Equilibrium optimizer
Exploitation
Heuristic
Humanities and Social Sciences
Image processing
Image segmentation
Magnetic resonance imaging
Multi-level thresholding
multidisciplinary
Mutation
Nonparametric statistics
Optimization algorithms
Outdoor air quality
Remote sensing
Science
Science (multidisciplinary)
Image segmentation
Multi-level thresholding
Equilibrium optimizer
ISSN:2045-2322, 2045-2322
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Abstract Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).
AbstractList Abstract Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).
Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).
Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).
ArticleNumber 29728
Author Hu, Pei
Zhang, Zheng
Han, Yibo
Chu, Shu-Chuan
Pan, Jeng-Shyang
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  fullname: Hu, Pei
  organization: School of Computer and Software, Nanyang Institute of Technology
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  givenname: Yibo
  surname: Han
  fullname: Han, Yibo
  organization: School of Computer and Software, Nanyang Institute of Technology
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  givenname: Zheng
  surname: Zhang
  fullname: Zhang, Zheng
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  givenname: Shu-Chuan
  surname: Chu
  fullname: Chu, Shu-Chuan
  organization: College of Artificial Intelligence, Nanjing University of Information Science and Technology
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  givenname: Jeng-Shyang
  surname: Pan
  fullname: Pan, Jeng-Shyang
  email: jengshyangpan@gmail.com
  organization: College of Artificial Intelligence, Nanjing University of Information Science and Technology, Department of Information Management, Chaoyang University of Technology
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Keywords Image segmentation
Multi-level thresholding
Equilibrium optimizer
Language English
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Snippet Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains...
Abstract Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it...
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SubjectTerms 631/114/1305
639/705/117
Algorithms
Brain research
Computer vision
Entropy
Equilibrium
Equilibrium optimizer
Exploitation
Heuristic
Humanities and Social Sciences
Image processing
Image segmentation
Magnetic resonance imaging
Multi-level thresholding
multidisciplinary
Mutation
Nonparametric statistics
Optimization algorithms
Outdoor air quality
Remote sensing
Science
Science (multidisciplinary)
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Title A multi-level thresholding image segmentation algorithm based on equilibrium optimizer
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