Abstract
Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.
Highlights
Image segmentation is fundamental and challenging work in computer vision, pattern recognition, and image processing
This paper proposes a novel variant of slime mould algorithm (SMA) (ESMA) with the Levy flight and quasi opposition-based learning to tackle these shortcomings and obtain high-quality threshold values in image segmentation
To verify the performance of the proposed enhanced slime mould algorithm (ESMA), we compared it with seven other algorithms including slime mould algorithm (SMA) [35], remora optimization algorithm (ROA) [36], arithmetic optimization algorithm (AOA) [32], aquila optimizer (AO) [33], salp swarm algorithm (SSA) [30], whale optimization algorithm (WOA) [29], and sine cosine algorithm (SCA) [31]
Summary
Image segmentation is fundamental and challenging work in computer vision, pattern recognition, and image processing. Renyi’s entropy was the objective fitness function All of these works are examples of meta-heuristic algorithms applied in multilevel thresholding image segmentation. Ewees et al [45] integrated the SMA and firefly algorithm to improve the performance for feature selection While these proposed improved versions of the SMA algorithm are better than the original SMA algorithm on specific problems, when solving multilevel thresholding image segmentation, the imbalance between exploration and exploitation is still an unavoidable problem. This paper proposes a novel variant of SMA (ESMA) with the Levy flight and quasi opposition-based learning to tackle these shortcomings and obtain high-quality threshold values in image segmentation. ESMA based on Levy flight and quasi opposition-based learning for solving global optimization problems and multilevel thresholding image segmentation.
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