A novel medical image fusion by combining TV-L1 decomposed textures based on adaptive weighting scheme

Abstract

Medical image fusion involves combining multiple images of diverse modalities to acquire superior image quality while retaining the information of an individual image. The fusion process aids physicians to diagnose and assess the disease by increasing the visual information and clarity. Direct fusion methods often generate undesirable effects leading to distortion and poor contrast. To some extent, multi-scale decomposition (MSD) methods have achieved success in various image fusion problems. However, they suffer from ringing artifacts, due to the blur caused by strong edges in decomposition steps. Hence, to overcome these drawbacks, we propose an efficient and novel image fusion algorithm for constructing a fused image through total variation (TV-L1) model using an optimized adaptive weighting scheme. Our method focuses on transferring the prominent textural details of Magnetic Resonance Imaging (MRI) and visual details of Positron Emission Tomography (PET) by cartoon and texture decomposition. It consists of data fidelity term to force the cartoon component to remain close to original image and a regularization term to transfer gradients from original image to the fused image. The present work preserves structural and functional details with high contrast. Experiments were conducted using MRI and PET images collected from standard Brain Atlas Datasets. The qualitative and quantitative analysis of the work suggest that the proposed method achieves better quality and accuracy as compared with the available state-of-the-art techniques. Keywords: TV-L1, Particle swarm optimization, Image fusion, MRI, PET