ArticleBiomimetics (Basel, Switzerland)2024
Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer.
Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Spatially context aware multilevel color image segmentation using a Hybrid Artificial Hummingbird and Great Deluge mechanism.Scientific reports · 2026Article
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3 authors.
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Abstract
The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. To acquire the optimal thresholds at different levels, we modify the GWO (MGWO) in terms of leader selection, position update, and mutation. We also use the Otsu method and Kapur entropy as objective functions. The performance of MGWO is compared with other color image segmentation algorithms on ten images from the BSD500 dataset in terms of objective values, variance, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental and non-parametric statistical analyses demonstrate that MGWO performs excellently in the multi-level thresholding segmentation of color images.
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