Perceptual medical image fusion with internal generative mechanism

Abstract

Medical image fusion is the process of integrating two medical images with a visual enhanced single fused image, to attain a resultant image richer in information to aid medical practitioners in better diagnosis. A perceptual medical image fusion method is proposed by employing Internal Generative Mechanism. First, source images are divided into a predicted layer and a detail layer with a Bayesian prediction model. Then, the detail layer is merged with the energy of Tchebichef moments for blocks while the predicted layer is fused using the averaging strategy as activity level measurement. The fused image is finally obtained by merging coefficients in both fused layers. Experimental results prove that the proposed fusion algorithm is superior to the previously developed methods.