Improved computer vision analysis for MRI and CT images using multistage fusion networks

Fusing different medical images will increase the accuracy in diagnosis of disease and describe the complicated relationship between them for medical research. The existing methods are time consuming and also require more number of samples to train the models. The proposed model will retrieve the complicated information from different medical images like Magnetic Resonance Imaging (MRI) together with Computed Tomography (CT)[1] by fusing them through multi-stage fusion networks. The proposed model will use Dual Tree Complex Wavelet Transform (DTCWT) to take out the complicated and correlated information from each images and segmentation is done on fused image to get the segmented image. In the proposed method, improved standard of final fused image can be obtained.