首页|Blind image quality assessment of magnetic resonance images with statistics of local intensity extrema
Blind image quality assessment of magnetic resonance images with statistics of local intensity extrema
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 Elsevier Inc.Magnetic resonance (MR) imaging provides a large amount of data that requires a visual inspection before a diagnosis can be made. Since the exclusion of low-quality image sequences is performed manually and image processing methods are evaluated using techniques developed for natural images, automatic and reliable MR image quality assessment (IQA) approaches are desirable. Therefore, in this work, a new no-reference (NR) MR-IQA technique is proposed. The method uses introduced quality-aware features addressing characteristics of MR images. Specifically, in the method, an MR image is scaled, filtered with two gradient operators, and subjected to identification of the local intensity extrema. Then, the entropy and κ curvature are calculated to characterize extrema sequences and used as perceptual features to train a quality model with the Support Vector Regression (SVR) technique. In this paper, an extensive comparative evaluation of the method against recent NR approaches, including deep learning-based models, is conducted on two representative MR-IQA benchmarks. The results reveal the superiority of the introduced approach over competing methods as it obtained better overall Spearman and Pearson correlation coefficients by 5% and 3%, respectively.
Oszust M.、Bielecka M.、Bielecki A.、Stepien I.、Obuchowicz R.、Piorkowski A.
展开 >
Department of Computer and Control Engineering Rzeszow University of Technology
Faculty of Geology Geophysics and Environmental Protection Chair of Geoinformatics and Applied Computer Science AGH University of Science and Technology
Faculty of Electrical Engineering Automation Computer Science and Biomedical Engineering Chair of Applied Computer Science AGH University of Science and Technology
Doctoral School of Engineering and Technical Sciences at the Rzeszow University of Technology
Department of Diagnostic Imaging Jagiellonian University Medical College
Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology
NSTL
Elsevier
