Medical image quality assessment via contrast masking

The human visual system (HVS) is one of the most important factor for image quality assessment (IQA). The IQA approaches integrating the characteristics of HVS are considered as the more reasonable and more effective approaches to obtain the image quality. In this paper, we propose an improved structural similarity metric (SSIM) for the medical images. The proposed method utilizes the visual sensitivity change in the different image regions to weight the quality map, which is obtained via integrating the contrast masking (CM) characteristic into the SSIM-based framework and called C-SSIM. Furthermore, we build a medical image quality assessment database for further testifying the effectiveness of our approach. The experimental result of our approach correlates well with human subjective opinions of image quality.