A novel algorithm for image segmentation (IP-MH-MLT): employing an image partitioning technique with metaheuristic parameters to enhance multilevel thresholding

The multilevel threshold technique of image segmentation is a popular and intriguing domain in the field of image vision and has received a lot of attention in several image processing applications due to its use in numerous image applications for assisting with a variety of problems. The key issue in this domain is figuring out the optimal number of thresholds and their values due to the limitations of conventional algorithms, such as fixed threshold values, a lack of adaptability, manual parameter setting, and a lack of contextual information. In order to deal with this problem, a new multilevel thresholding (MLT) algorithm (IP-MH-MLT) has been proposed in this paper. It is based on the image partitioning (IP) approach and has a few parameters that are computed using any metaheuristic (MH) technique, and the remaining parameters are evaluated through image characteristics. In this paper, for the metaheuristic parameter, a swarm-based metaheuristic called Grey Wolf Optimizer (GWO) is taken into consideration due to its numerous features, such as simplicity and ease of implementation, efficient convergence speed, and limited computational complexity. The performance of the proposed algorithm has been validated on a set of fifteen benchmark images using various thresholds and compared quantitatively in terms of a number of performance metrics, including structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), with eight different metaheuristic algorithms. In order to examine the proposed algorithm qualitatively, Friedman ranking tests are carried out on the proposed algorithm over other comparable algorithms. The results demonstrate the effectiveness and competitive performance of the proposed algorithm. Graphical abstract