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No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features.

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Title: No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features.
Authors: Oszust, Mariusz1 (AUTHOR) marosz@kia.prz.edu.pl, Piórkowski, Adam2 (AUTHOR), Obuchowicz, Rafał3 (AUTHOR)
Source: Magnetic Resonance in Medicine. Sep2020, Vol. 84 Issue 3, p1648-1660. 13p.
Subjects: Magnetic resonance imaging, Image processing, Eye, Magnetic resonance
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Sep2020, Vol. 84 Issue 3, p1648-1660. 13p.
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Sep2020
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