Abstract
Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.
Highlights
Introduction iationsImage segmentation is one of the primary and essential operations for pattern recognition and image analysis
The proposed artificial ecosystem-based optimization differential evolution (AEODE) method is tested besides other optimization algorithms, such as the basic artificial ecosystem-based optimization (AEO), marine predators algorithm (MPA), gray wolf optimization (GWO), spherical search optimization (SSO), cuckoo search (CS), and grasshopper optimization algorithm (GOA)
The AEODE obtained the best values in the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) measure, and it achieved the fourth rank after AEO, MPA, and GOA in the fitness function measure
Summary
One of the most vital techniques utilized in the computer vision domain is multilevel thresholding. The results revealed that the DCPSO algorithm obtained the optimal number of clusters on the experimented image datasets, compared to other similar methods, such as GA and the conventional PSO algorithm. The KnEA was assessed, utilizing several images tested at six various threshold levels, and the results were compared to several existing many-objective optimization techniques. In [24], the authors proposed a multi-objective optimization method, using the MVO algorithm for gray-scale image segmentation by multi-thresholding values. We notice that optimization-based multilevel thresholding image segmentation is considered an emerging research field with new and exciting theories and strategies It is recognized as a critical problem to employ various techniques with various objective functions over varying models of images
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