Multimodal Medical Image Fusion Enhancement Based on Wavelet Transform

Abstract

Image fusion is the process of combining multiple images to obtain one image with complementary information. Hence the importance of multimodal medical image fusion, which aims to obtain all the important information in one image to improve diagnosis in the medical field. The wavelet transform is a mathematical tool for multi-resolution analyses where the images are decomposed into a series of subbands. Wavelet transforms have a good representation of the original images. The averaging fusion rule and shift variance in the discrete wavelet transform, on the other hand, make the image that comes out of fusion under the wavelet transform blurry or of low quality. In this paper, two novel methods have been presented to improve the quality of the multimodal medical image fusion-based wavelet transform. The efficiency of the proposed methods has been measured using three metrics, all of which prove the efficiency of the proposed methods compared to the standard wavelet methods.