A chaotic variant of the Golden Jackal Optimizer and its application for medical image segmentation

The initial segmentation phase is crucial in image processing to simplify the image representation and extract some desired features. Different methods and techniques have been proposed for image multi-level thresholding, but they are still stuck in local optima and need improvement. Recently, a metaheuristic optimization algorithm called Golden Jackal Optimizer (GJO) has been proposed as an alternative solution. The GJO has been adopted as a good solution for many optimization problems. However, the GJO attempted to solve the convergence problem to a local minimum during execution, often leading to unsatisfactory results. Most variants of GJO are based on chaotic systems due to their easy implementation and remarkable capacity to avoid being trapped in local optima. This paper proposes a Polynomial Chebychev Symmetric Chaotic-based GJO (PCSCGJO) algorithm by combining a recently developed chaotic generating function to achieve better segmentation results. This variant improves the GJO by introducing the chaotic generating function of the Chebyshev polynomials as an update process while searching for the optimal solution. Simulation results prove the effectiveness of the PCSCGJO method and its ability to deal with different medical color images. The quality of the segmented images obtained by the proposed method was compared to well-known metaheuristic algorithms using performance metrics such as PSNR, SSIM, FSIM, and MSE. Consequently, the metrics values show that the suggested technique outperforms the other methods regarding quality and accuracy.