Hybrid SCCSA: An efficient multilevel thresholding for enhanced image segmentation

In a variety of image processing applications, multilevel thresholding image segmentation has gotten a lot of interest. When traditional approaches are utilised, however, the process of obtaining the ideal threshold values takes time. Despite the fact that Hybrid metaheuristic methods can be used to overcome these limits, such approaches may be ineffective when dealing with a local solution. The present study proposes a multi-level image thresholding based hybridization strategy based Sine-Cosine Crow Search Algorithm(SCCSA) to make more efficient image segmentation. The main limitation of the classical Crow Search Algorithm (CSA) is that search agents sometimes do not produce the best solutions. To update a solution to the best solution, each search agent can use Sine-Cosine Algorithm (SCA) movements to update its position accordingly. This ensures a good balance between two goals (exploration and exploitation) would improve the efficiency of the search algorithm. The optimal threshold values are searched by the chosen objective functions of the otsu’s and kapur’s entropy approaches. The hybrid algorithm is evaluated in 12 standard image sets and then compared with the performance of other state-of-the-art algorithms such as ICSA, SCA, CSA and ABC. Experimental results showed that, in different metrics of the output such as objective function values, PSNR, STD values, Mean, SSIM, FSIM and CPU time, the proposed algorithm is consistently higher than other algorithms. In addition, the wilcoxon test is performed using the proposed algorithm to detect the significant differences between the other algorithms. The findings indicated that the proposed SCCSA succeeds with other well-known algorithms and has dominance over robust, accurate and convergent values.