Research on medical image denoising using an improved dung beetle optimization algorithm based on a novel 3D chaotic system
Yumin Dong, Tonglei Sun · Physica Scripta · 2025
Abstract Artifacts and noise not only reduce the imaging quality of medical images but also threaten the integrity and accuracy of medical data features. Therefore, image denoising has become an indispensable step in the medical imaging process. However, previous medical image denoising methods have shown certain limitations when applied to different types of images. For example, using filtering methods for image denoising often results in edge blurring and even loss of image details. This significantly affects the integrity and accuracy of medical data features when processing detail-rich medical images, such as brain images.To address these limitations, this paper uses the improved dung beetle optimization algorithm of 3D chaotic system combined with hybrid filter to achieve better denoising effect. The newly proposed 3D chaotic system (3D-GP) enhances the randomness of the initial population position in the dung beetle optimization algorithm, ensuring that the search space follows a symmetric probabilistic distribution, thus preventing the DBO algorithm from falling into local optima. The improved algorithm exhibits a high degree of adaptability, dynamically adjusting parameter settings based on the characteristics of different medical images to achieve more consistent and stable denoising results. Based on the improved DBO algorithm, the incorporation of hybrid filters enables effective handling of various types of noise. Specifically, for complex medical images with speckle noise and Gaussian noise, the proposed algorithm demonstrates remarkable denoising effectiveness. Experimental results show that the improved DBO algorithm can effectively remove noise while preserving image details, especially when processing images with subtle structures and strong noise interference. Compared with traditional filtering methods, the proposed algorithm not only improves the imaging quality of medical images but also ensures the integrity and accuracy of medical data features.