This article presents a novel approach for enhancing medical image segmentation using particle swarm optimization (PSO) combined with histogram equalization (HE). The study evaluates the effectiveness of this method on lung CT scan and chest X-ray datasets, focusing on improving segmentation accuracy and robustness. The PSO algorithm is used to optimize the segmentation process, while HE preprocessing enhances image contrast and visibility, making it more suitable for accurate segmentation. The integration of PSO and HE addresses the challenges of noise, non-uniform illumination, and complex anatomical structures in medical images. The study demonstrates that HE preprocessing significantly improves segmentation accuracy, as evidenced by enhanced evaluation metrics such as accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard index. Comparative analyses with alternative methods like Otsu, Watershed, and K-means confirm the competitiveness of the PSO-HE approach, particularly for chest X-ray images. The results show that HE preprocessing leads to more stable convergence and better segmentation outcomes, especially for complex lung CT scan images. The method involves preprocessing steps including image conversion to 8-bit, grayscaling, histogram equalization, and image adjustment. PSO is then applied to partition the preprocessed image into distinct regions, with the objective function guiding the optimization process to find the optimal segmentation. The integration of PSO and HE ensures that the segmentation process is both accurate and efficient, addressing the limitations of traditional segmentation methods. The study highlights the importance of preprocessing in enhancing image quality and segmentation accuracy. The PSO-HE approach is shown to be effective in improving the precision and reliability of medical image segmentation, offering a promising solution for advancing healthcare applications. The findings suggest that the PSO-HE method is a valuable tool for medical image analysis, with potential for further research and integration with other advanced techniques.This article presents a novel approach for enhancing medical image segmentation using particle swarm optimization (PSO) combined with histogram equalization (HE). The study evaluates the effectiveness of this method on lung CT scan and chest X-ray datasets, focusing on improving segmentation accuracy and robustness. The PSO algorithm is used to optimize the segmentation process, while HE preprocessing enhances image contrast and visibility, making it more suitable for accurate segmentation. The integration of PSO and HE addresses the challenges of noise, non-uniform illumination, and complex anatomical structures in medical images. The study demonstrates that HE preprocessing significantly improves segmentation accuracy, as evidenced by enhanced evaluation metrics such as accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard index. Comparative analyses with alternative methods like Otsu, Watershed, and K-means confirm the competitiveness of the PSO-HE approach, particularly for chest X-ray images. The results show that HE preprocessing leads to more stable convergence and better segmentation outcomes, especially for complex lung CT scan images. The method involves preprocessing steps including image conversion to 8-bit, grayscaling, histogram equalization, and image adjustment. PSO is then applied to partition the preprocessed image into distinct regions, with the objective function guiding the optimization process to find the optimal segmentation. The integration of PSO and HE ensures that the segmentation process is both accurate and efficient, addressing the limitations of traditional segmentation methods. The study highlights the importance of preprocessing in enhancing image quality and segmentation accuracy. The PSO-HE approach is shown to be effective in improving the precision and reliability of medical image segmentation, offering a promising solution for advancing healthcare applications. The findings suggest that the PSO-HE method is a valuable tool for medical image analysis, with potential for further research and integration with other advanced techniques.