CT and MRI image fusion algorithm based on hybrid [formula omitted] layer decomposing and two-dimensional variation transform

Abstract

Image fusion of CT and MRI significantly improves the diagnosis of diseases. This study proposes a novel CT and MRI image fusion algorithm based on hybrid ℓ0ℓ1 layer decomposition and two-dimensional variation transforms to reduce the loss of complementary features. From the characteristics of CT and MRI images, the two-dimensional variation transform is used to separate the low-frequency and high-frequency features of the MRI image to preserve all detailed features of MRI. Then, the separated low-frequency feature images and the CT image are transformed by hybrid ℓ0ℓ1 layer decomposing to separate the edge features and contour features. Furthermore, the separated feature images are synthesized separately to obtain the fusion images of different features such as edge and contour feature fusion images. Finally, the fusion images of different features are reconstructed to obtain the final fusion images. Experiments show that the fusion algorithm better fuses the complementary features of different images and reduces the loss of information.