Medical image fusion method based on saliency measurement improvement and local structure similarity correction

Abstract

Medical image fusion is an important tool in medical diagnosis, because the quality of the fused image is closely related to the diagnosis result. However, the approaches of highlighting details through the acquisition of high-contrast lead to information distortion in the fused image and then increase the misdiagnosis rate. In order to solve the problem, a medical image fusion method based on saliency measurement improvement and local structural similarity correction is proposed in this paper. First, multi-level decomposition latent low-rank representation (MDLatLRR) combined with nonsubsampled shearlet transformation (NSST) is presented to decompose source images to obtain high SNR base layers and multi-scale detail layers. Then, the saliency measurement improvement with the source image mask filtering is advanced for base layer fusion rule. Specially, after the base layer is refined by the source image mask for the salient area extraction, the salient area is refreshed by its mean gray to calculate the fusion coefficient. Finally, a correction method based on structural similarity index measure (SSIM) is put forward to deal with the information distortion in the initial fused image, in which the correction weight is acquired through the weighted structural similarity between the initial fused image and the source ones. Experiments show that our method has excellent subjective visual effects, and outperforms the other states-of-the-art methods in objective indexes.