Oszust Mariusz, Piórkowski Adam, Obuchowicz Rafał
Department of Computer and Control Engineering, Rzeszów University of Technology, Rzeszów, Poland.
Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland.
Magn Reson Med. 2020 Sep;84(3):1648-1660. doi: 10.1002/mrm.28201. Epub 2020 Feb 12.
Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques.
A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve low-frequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method.
The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments.
It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development.
对显示的磁共振（MR）图像进行主观质量评估在诊断及后续治疗中起着关键作用。因此，本研究旨在引入一种新的无参考（NR）图像质量评估（IQA）方法，用于对MR图像进行客观、自动的评估，并将其判断结果与类似技术的结果进行比较。
开发了一种新颖的NR - IQA方法。该方法使用一系列经过缩放的图像，这些图像经过滤波以增强高频分量并保留低频部分。由于人类视觉系统（HVS）对局部图像变化敏感，且局部特征通常会模拟HVS对高频图像区域的吸引力，因此在滤波后的图像中检测并描述这些局部特征。然后，通过支持向量回归方法，利用所获得描述符的统计数据构建质量模型。
在一个由31名经验丰富的放射科医生评估的包含70幅失真MR图像的新数据集上，使用NR度量比较的典型评估标准，将该方法与21种最先进的NR - IQA技术进行了比较。就与人类判断的相关性而言，所引入的方法显著优于所比较的方法。
结果表明，所提出的用于评估MR图像的NR - IQA方法优于最先进的NR技术。该方法将有利于广泛的图像处理应用，评估其输出并影响其发展方向。
