Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis
An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in...
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MDPI AG
2020-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/2/220 |
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| author | Rafał Obuchowicz Mariusz Oszust Marzena Bielecka Andrzej Bielecki Adam Piórkowski |
| author_facet | Rafał Obuchowicz Mariusz Oszust Marzena Bielecka Andrzej Bielecki Adam Piórkowski |
| author_sort | Rafał Obuchowicz |
| collection | DOAJ |
| description | An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores. |
| first_indexed | 2024-04-11T21:49:16Z |
| format | Article |
| id | doaj.art-315483698726433788bc885d4c964cad |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T21:49:16Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-315483698726433788bc885d4c964cad2022-12-22T04:01:18ZengMDPI AGEntropy1099-43002020-02-0122222010.3390/e22020220e22020220Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy AnalysisRafał Obuchowicz0Mariusz Oszust1Marzena Bielecka2Andrzej Bielecki3Adam Piórkowski4Department of Diagnostic Imaging, Jagiellonian University Medical College, 19 Kopernika Street, 31-501 Cracow, PolandDepartment of Computer and Control Engineering, Rzeszow University of Technology, W. Pola 2, 35-959 Rzeszow, PolandFaculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, PolandFaculty of Electrical Engineering, Automation, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, PolandDepartment of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, PolandAn investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.https://www.mdpi.com/1099-4300/22/2/220blind image quality assessmentmagnetic resonance imagesentropynon-maximum suppression |
| spellingShingle | Rafał Obuchowicz Mariusz Oszust Marzena Bielecka Andrzej Bielecki Adam Piórkowski Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis Entropy blind image quality assessment magnetic resonance images entropy non-maximum suppression |
| title | Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis |
| title_full | Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis |
| title_fullStr | Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis |
| title_full_unstemmed | Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis |
| title_short | Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis |
| title_sort | magnetic resonance image quality assessment by using non maximum suppression and entropy analysis |
| topic | blind image quality assessment magnetic resonance images entropy non-maximum suppression |
| url | https://www.mdpi.com/1099-4300/22/2/220 |
| work_keys_str_mv | AT rafałobuchowicz magneticresonanceimagequalityassessmentbyusingnonmaximumsuppressionandentropyanalysis AT mariuszoszust magneticresonanceimagequalityassessmentbyusingnonmaximumsuppressionandentropyanalysis AT marzenabielecka magneticresonanceimagequalityassessmentbyusingnonmaximumsuppressionandentropyanalysis AT andrzejbielecki magneticresonanceimagequalityassessmentbyusingnonmaximumsuppressionandentropyanalysis AT adampiorkowski magneticresonanceimagequalityassessmentbyusingnonmaximumsuppressionandentropyanalysis |