A Novel MRI And CT Image Fusion Based on Discrete Wavelet Transform and Principal Component Analysis for Enhanced Clinical Diagnosis

Abstract

A large percentage of healthcare resources, including imaging tools, like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been dedicated to the management of affected patients in this pandemic of Coronavirus disease 2019 (COVID-19). The diagnostic modalities in medical research are improving at a rapid pace with an objective to acquire maximum information with as little data as possible without any artifacts. That is where image fusion comes into the picture. It is a technique of merging source medical pictures to maximize the necessary information. CT is generally used for bony structures, whereas MRI is more appropriate for soft tissues. A fusion of MRI and CT images would lead to enhancement of the overall image quality while giving comprehensive information, at the same time artifacts are also eliminated. Image fusion methods are applied in medical science and various other sectors. Several image processing techniques are used in medical diagnostics, like Principal Component analysis (PCA), Intensity-Hue-Saturation, Discrete Wavelet Transform (DWT), and others. This study suggests an image fusion algorithm utilising the principal component averaging and the DWT along with the performance analysis of the fusion of the MRI and CT images of brain. The technique used in our study significantly enhances the image quality in terms of various fusion performance measures that helps the medical practitioners to diagnose any infection and aids in its treatment.