Multilevel thresholding using K-point strategy improved convergence based whale optimization algorithm for image segmentation

The current study presents an innovative multilevel image segmentation method utilizing an improved Whale Optimization Algorithm (WOA). While WOA has shown promise in various optimization tasks, its performance can be limited by a tendency to be trapped in local optima. To address this challenge, the K-point Strategy Improved Convergence WOA (KSICWOA), which enhances optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization strategy. The proposed KSICWOA is then applied alongside Otsu's cross variance and Kapur's entropy as objective functions to determine optimal thresholds for multilevel grayscale image segmentation. Experimental results on benchmark functions as well as real-time images demonstrate that KSICWOA surpasses conventional optimization techniques in terms of search accuracy and convergence speed while effectively avoiding local optima. It provides an average improvement of 28.3%, 25.61%, and 7.1% in terms of PSNR, SSIM, and FSIM over the WOA method. Additionally, tests conducted on standard image segmentation datasets confirm that the KSICWOA-Kapur method accurately and efficiently identifies multilevel thresholds.