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No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features
Author(s) -
Oszust Mariusz,
Piórkowski Adam,
Obuchowicz Rafał
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28201
Subject(s) - artificial intelligence , computer science , image quality , pattern recognition (psychology) , computer vision , image processing , support vector machine , image (mathematics) , quality (philosophy) , human visual system model , magnetic resonance imaging , medicine , radiology , philosophy , epistemology
Purpose 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. Methods 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. Results 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. Conclusions 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.