A New Approach to Medical Image Fusion Based on the Improved Extended Difference-of-gaussians Combined with the Coati Optimization Algorithm

The synthesis of medical images plays a pivotal role in image-based disease diagnosis. In recent years, numerous medical image synthesis methods have been proposed. Nevertheless, images generated from the proposed synthesis methods often suffer from shortcomings, including low image quality, reduced brightness and contrast, and loss of vital information. In this paper, we propose a novel approach to tackle the aforementioned challenges in medical image synthesis. Initially, the input images are decomposed into two components: low-frequency and high-frequency components using the Weighted mean curvature filter (WMCF). Subsequently, we propose a synthesis rule for the high-frequency components based on the combination of the Extended difference-of-Gaussians (XDoG) filter, the Structure tensor (ST), and the Local energy (LE) function. Additionally, we employ a novel adaptive synthesis rule, based on the Coati optimization algorithm (COA), to synthesize the low-frequency components. We conducted four experiments using 90 pairs of medical images. The experimental results demonstrate that our proposed method not only effectively enhances image quality, brightness, and contrast but also better preserves crucial details such as boundaries, edges, and the original image’s structure when compared to the most recently published methods.