Analysis of Medical Image Fusion Using Transform-Based Function and Neural Network

Abstract

Medical image fusion plays a significant role in computer-aided diagnosis of critical illness and disease. The continuous support of computer vision and medical science, image fusion methods are improved. In the improvement of image fusion algorithm, transform-based function and neural network is a significant participant. The transform-based function follows the categories of feature-based image fusion. The transform function such as DCT, DWT, CT, and other transform function variants applied for the extraction of features. The transform-based process is texture dominated features transform. The texture is important features of medical imagery; the coverage area of texture features is 75% in the whole image.  The relation of neural network and image fusion has a very long time. The neural network methods improve the fusion efficiency of medical image and produce good quality results in terms of PSNR and SIM. This paper presents the experimental analysis of various transform and neural network methods for medical image fusion. The study used standard medical image fusion dataset and measured standard parameters such as PSNR, SIM. The analysis process used MATLAB software, and this is a well-known software for the neural network and image processing.