Medical Image Fusion Using Unified Image Fusion Convolutional Neural Network

Abstract

Medical image fusion (IF) is a process of registering and combining numerous images from multiple-or single-imaging modalities to enhance image quality and lessen randomness as well as redundancy for increasing the clinical applicability of the medical images to diagnose and evaluate clinical issues. Te information that is acquired additionally from fused images can be efectively employed for highly accurate positioning of abnormality. Since diverse kinds of images produce various information, IF becomes more complicated for conventional methods to generate fused images. Here, a unifed image fusion convolutional neural network (UIFCNN) is designed for IF utilizing medical images. To execute the IF process, two input images, namely, native T1 and T2 fuid-attenuated inversion recovery (T2-FLAIR) are taken from a dataset. An input image-T1 is preprocessed employing bilateral flter (BF), and it is segmented by a recurrent prototypical network (RP-Net) to obtain segmented output-1. Simultaneously, input image-T2-FLAIR is also preprocessed by BF and then segmented using RP-Net to acquire segmented output-2. Te two segmented outputs are fused utilizing the UIFCNN that is introduced by assimilating unifed and unsupervised end-to-end IF network (U2Fusion) with IF framework based on the CNN (IFCNN). In addition, the UIFCNN obtained maximal Dice coefcient and Jaccard coefcient of 0.928 and 0.920 as well as minimal mean square error (MSE) of 0.221.