Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion

Multimodal medical image fusion technique contributes to the reduction of information uncertainty and improves the clinical diagnosis accuracy, the aim of which is to preserve salient image features and detail information of multiple source images to produce a visual enhanced single fused image. In this paper, by taking full advantages of structure tensor and nonsubsampled shearlet transform (NSST) to effectively extract geometric features, a novel unified optimization model is proposed for fusing computed tomography and magnetic resonance imaging images. The proposed model includes two terms, which constrain the gradient and NSST coefficients of the final fused image close to the pre-fused gradient and NSST coefficients. The pre-fused gradient is obtained from weighted structure tensor and the pre-fused NSST coefficients are generated by fusing NSST coefficients of source images with proposed fusion rules. The final fused image can be obtained by solving the constructed optimization problem via conjugate gradient method. Experimental results demonstrate that the proposed approach outperforms the compared multi-resolution and gradient based methods in both visual effects and quantitative assessments.