Fusion of CT and MRI Medical Images Using Dual Tree Complex Wavelet Transform

Abstract

Abstract: Combining various medical pictures will improve illness diagnosis accuracy and illustrate the complex link between them for medical study. Existing approaches take a long time and require a large number of data to train the models. In this model, we will use multi-stage fusion networks to extract sophisticated information from medical pictures such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). In the suggested approach, we will extract the difficult and correlated information from each picture using the Dual Tree Complex Wavelet Transform (DTCWT), and then segment the fused image to obtain the segmented image. The proposed approach entails, the fusion of multimodal medical pictures may be accomplished using the Dual Tree Complex Wavelet Transform, which converts the original medical image to grayscale and decomposes it before extracting the wavelet coefficients using DTCWT. Following that, wavelet approximation is utilized to produce the fused coefficients. To obtain a final fused picture, the Inverse Dual Tree Complicated Wavelet Transform is performed. Additionally, segmentation is carried out in order to provide a segmented image for better visual representation. The quality of the final fused picture can be increased using the proposed strategy.