Abstract
Segmentation of medical images is a crucial step in medical diagnosis, essential for accurate disease detection and treatment planning. However, traditional multi-threshold image segmentation techniques often face challenges such as high computational demands and susceptibility to local optima. This study aims to address these challenges by introducing an enhanced optimization algorithm, GBJAYA, which integrates the Gaussian bare-bone strategy to improve segmentation performance. The proposed GBJAYA algorithm incorporates Gaussian-distributed random number update mechanisms to enhance global search capabilities and accelerate convergence. The algorithm's effectiveness was evaluated through experiments on IEEE CEC2017 benchmark functions and two types of medical images. Performance was assessed using metrics such as PSNR, SSIM, and FSIM, and statistical validation was conducted using Friedman and Wilcoxon tests. The results demonstrate that GBJAYA outperforms 10 basic and 10 improved algorithms, achieving lower mean values and smaller standard deviations in most tests. The algorithm exhibited superior segmentation performance and stability, as confirmed by convergence curve analysis, which also highlighted its rapid convergence and ability to avoid local optima. The GBJAYA significantly enhances medical image segmentation, offering superior performance, stability, and fast convergence. These findings demonstrate its broad potential for application in medical diagnosis and treatment planning.