Image thresholding has different limitations especially with the methods used to search the best configuration of thresholds. To avoid these drawbacks, the meta-heuristic algorithms are commonly used, they have the ability to find the global solution in a reduced number of iterations. Based on this concept, this chapter presents an improvement of the salp swarm algorithm based on artificial bee colony as an alternative image segmentation method. The proposed method combines the operators of the ABC with SSA and this lead to improve the convergence and find the best threshold value. The proposed method, called SSAABC, uses the Kapur’s function to assess the quality of each solution. In order to evaluate the performance of the proposed approach six images are used as test and the results are compared with four different algorithms. The Experimental results provides an evident about the high performance of the proposed SSAABC method in terms of the performance measures such as PSNR, SSIM, and CPU time(s).