Medical image fusion based on enhanced three-layer image decomposition and Chameleon swarm algorithm

Medical image fusion has brought practical applications in clinical diagnosis. However, image fusion methods still face challenges because of problems with the quality of the input images. Phenomena such as noise or low contrast can appear in the original images, which significantly degrades the quality of the synthesized image. Most current image synthesis algorithms do not thoroughly focus on solving the image quality problem. Therefore, if the input images are noisy or low-contrast, it will significantly affect the resulting image synthesis. This study proposes a new image synthesis method that allows efficient operation even when the input image is noisy or has low contrast. Firstly, we propose a new image enhancement algorithm that focuses on solving the problem of noise or low contrast of the input image. This enhancement algorithm is built based on several methods, such as Contrast-limited adaptive histogram equalization (CLAHE), Block-matching and 3D filtering (BM3D), and Chameleon swarm algorithm (CSA). Next, we introduce a method to decompose the image into three enhanced layers based on the adaptive parameters obtained from the proposed image enhancement method. This image decomposition method is used to decompose the input medical images into high-frequency and low-frequency layers. Finally, high-frequency layers are synthesized based on the CSA method, and low-frequency layers are synthesized based on the sum of the local energy functions using the Prewitt compass operator (SLE_PCO). One hundred eighty medical images, various imaging enhancement methods, and medical image synthesis were used for comparison and evaluation. Experimental results show that our image enhancement method works well with noisy and low-contrast images. Furthermore, our image fusion method gives the best performance when compared with the latest image synthesis methods available today.