ArticleScientific reports2024
A multi-level thresholding image segmentation algorithm based on equilibrium optimizer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A Fixed-Ratio Hybrid ARO-ALO Algorithm for Multi-Level Thresholding of Histopathological Colon Cancer Images.Cancers · 2026Article
- Spatially context aware multilevel color image segmentation using a Hybrid Artificial Hummingbird and Great Deluge mechanism.Scientific reports · 2026Article
- Prolonged expiratory time constant and risk of moderate-to-severe exacerbations in stable COPD.Scientific reports · 2026Article
- Optimized Lung Nodule Classification Using CLAHE-Enhanced CT Imaging and Swin Transformer-Based Deep Feature Extraction.Journal of imaging · 2025Article
- Multilevel thresholding of color images using globally informed artificial bee colony algorithm.Scientific reports · 2025Article
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5 authors.
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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).
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