Research Of Medical Image Quality Assessment

With the rapid development of the modern medical imaging equipment, both the new imaging methods and image processing methods are constantly emerging. The medical images have become a major reference in clinical research, diagnosis and treatment. The development of medical image quality assessment for monitoring and adjusting the quality of medical image, and testing and optimizing the algorithm of medical image processing is great significance.For all medical image quality assessment, the subjective assessment may be the best one. However, the subjective assessment is too inconvenient, costly and time-consuming for practical usage. Moreover, it is also easily affected by the subjective factors, and hard to be embedded in the medical image processing system. Therefore, how to assess the medical image quality is a very interesting topic.The peak signal-to-noise ratio (PSNR) is used as a major image quality assessment in the medical image processing system. As we all know, PSNR doesn't take into account correlation between pixels and the perceptual characteristics of human visual system, and the assessment results maybe not reflect the image quality of visual perception. Therefore, it is essential to research some new medical image quality assessment based on the human visual system.In this paper, we study the quality assessment of medical image which to be treated by the medical image processing method, which could be helpful for further medical image processing technique development. Firstly, we introduce the research background and significance, and describe two definitions about image quality. Secondly, we give the brief description of human visual system, and discuss the algorithm that is relative to the characteristics of the human visual system and human visual perception. Then, we outline the image quality assessment, introduce the subjective and objective image quality assessment, and highlights and analysis of the structural similarity based image quality assessment. Moreover, we summarize the application of the objective medical image quality assessment.In this paper, we study in-depth the effects of human visual properties on the image quality, and research SSIM proposed by Zhou Wang etc. We think that it is difficult to modeling exactly characteristics of the human visual system from the structure comparison function in SSIM. Therefore, we propose two new medical image assessments. The main contributions can be summed as follows:1. Improve the objective image quality assessment based on SSIM. In the improved algorithm, we take full advantage of the relation between image gradient, image edge and texture information, re-define the structure comparison function as the image gradient direction information, and the contrast comparison function as the gradient magnitude. The experiment results match greatly with the subjective assessment.2. Proposed a medical image assessment based on gradient-weighted SSIM (GWSSIM). Aiming at different quality effects on different image region and distortion type, we take full advantage of the visual masking effect and proposed a new weighted idea. This new weighted algorithm judges the image texture and distortion type by the image gradient. The experiment results fit well the subjective assessment.3. We construct the medical image database used to evaluate the performance of new objective image assessment methods. For the basic methods, medical image quality assessment is the same as general natural image. But whether the general natural image quality assessment is suitable for medical image must be experimented in simulation by medical image. To study further the better suitable objective medical image quality assessment, we construct the medical image database used to evaluate the performance of new objective image assessment methods.