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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Vydané v:Scientific reports Ročník 14; číslo 1; s. 29728 - 16
Hlavní autori: Hu, Pei, Han, Yibo, Zhang, Zheng, Chu, Shu-Chuan, Pan, Jeng-Shyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 29.11.2024
Nature Publishing Group
Nature Portfolio
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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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Shrnutí: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).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-81075-w