A Novel Approach Using the Local Energy Function and Its Variations for Medical Image Fusion

Medical image fusion plays a pivotal role in facilitating clinical diagnosis. However, the quality of input medical images may be marred by noise, low contrast, and lack of sharpness, presenting numerous challenges for medical image synthesis algorithms. Additionally, several fusion rules may degrade the brightness and contrast of the fused image. To this end, this paper presents a novel image synthesis approach to tackle the aforementioned issues. First, the input images undergo pre-processing to enhance their quality. Subsequently, we introduce the three-layer image decomposition (TLID) technique, which decomposes an image into three distinct layers: the base layer (L-B), the small-scale structure layer (L-SS), and the large-scale structure layer (L-LS). Next, we synthesize the base layers utilizing adaptive rules based on the Marine predators algorithm (MPA), ensuring that the output image is not degraded. Finally, we propose an efficient synthesis method for L-SS and L-LS layers, based on combining the local energy function with its variations. This fusion technique preserves the intricate details present in the original image. We evaluated our approach on 156 medical images using six evaluation metrics and compared it with seven state-of-the-art image synthesis techniques. Our results demonstrate that our method successfully generates high-quality output images and preserves detailed information throughout the image synthesis process.