Advanced Multi-Threshold Breast Cancer Image Segmentation Using an Enhanced Particle Swarm Optimizer

Abstract

Breast cancer remains a significant global health concern, necessitating the advancement of image segmentation techniques to improve diagnostic accuracy. Traditional thresholding methods often fail to effectively segment images due to complex cellular structures and indistinct boundaries. To address these challenges, this study proposes the Local Homogeneity-Based Reinitialization Particle Swarm Optimization (LHBRPSO) algorithm for multi-threshold segmentation, integrating local homogeneity analysis with adaptive threshold reinitialization to enhance segmentation accuracy by mitigating intra-class variability. The performance of LHBR-PSO is evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), ensuring a comprehensive assessment of both numerical accuracy and perceptual quality. Comparative analysis against conventional optimization-based segmentation methods demonstrates the superior performance of LHBR-PSO across various thresholding levels, while statistical validation using the Friedman ranking test further confirms its robustness, as it consistently achieves higher rankings in segmentation effectiveness. These findings underscore the potential of LHBR-PSO as a reliable and computationally efficient approach for image segmentation, offering an advanced solution for enhancing breast cancer diagnosis.