Medical fusion image quality assessment based on SSIM

Abstract

The assessment of image quality is important in numerous image processing applications. Commonly used measures, such as the mean squared error (MSE) and peak signal to noise ratio (PSNR), ignore the spatial information (e.g. redundancy) contained in natural images, which can lead to an inconsistent similarity evaluation from the human visual perception. image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image, the Structural Similarity Image (SSIM) operate under the assumption that human visual perception is highly adapted for extracting structural information from a scene. In this article, we propose a new similarity measure that replaces traditional methods to evaluate medical fusion image. Experimental results show that SSIM provides a more consistent image structural fidelity measure than commonly used measures and the consistency of people's subjective feeling better. © 2013 Springer Science+Business Media.