SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER
Magnetic resonance imaging (MRI) is a key method for imaging human tissues and organs. The accuracy of medical diagnosis is greatly affected by the quality of MRI images. Sometimes, MRI images are obtained blurry due to various inevitable constraints related to the imaging equipment, which affects t...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2024-12-01
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| Series: | Applied Computer Science |
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| Online Access: | https://ph.pollub.pl/index.php/acs/article/view/6577 |
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| author | Manar AL-ABAJI Zohair AL-AMEEN |
| author_facet | Manar AL-ABAJI Zohair AL-AMEEN |
| author_sort | Manar AL-ABAJI |
| collection | DOAJ |
| description | Magnetic resonance imaging (MRI) is a key method for imaging human tissues and organs. The accuracy of medical diagnosis is greatly affected by the quality of MRI images. Sometimes, MRI images are obtained blurry due to various inevitable constraints related to the imaging equipment, which affects the detection of important features in the image. Several sharpening methods were introduced, but not all were successful in this task, as artifacts may be introduced, contrast may be changed, and high complexity may be involved. Thus, this paper introduces a guided-subsumed unsharp mask filter (GSUM) to improve the sharpness of MRI images. The GSUM utilizes an improved guided filter instead of the low-pass Gaussian filter and a dynamic sharpening parameter. The improved guided filter employs a hybrid procedure instead of the mean filter in the smoothing process and relies on an adaptive regularization parameter. The applied modifications eliminated the overshooting and halo effects of the original unsharp masking and the guided filter, resulting in better-quality images. The GSUM was tested with real-blurry MRI images, evaluated using three no-reference metrics, and compared with six other algorithms. The metric scores indicate that the proposed filter can surpass existing methods, as it produced better results with average readings of 24.2074 in PIQE, 0.6878 in BLUR, and 5.7944 in FISH. It also scored a fast computation time, averaging 0.3384 seconds.
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| format | Article |
| id | doaj-art-9fc35190f2a64de79d2a650ba2f9f5b7 |
| institution | Kabale University |
| issn | 2353-6977 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj-art-9fc35190f2a64de79d2a650ba2f9f5b72025-01-09T12:44:45ZengPolish Association for Knowledge PromotionApplied Computer Science2353-69772024-12-0120410.35784/acs-2024-47SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTERManar AL-ABAJI0https://orcid.org/0000-0001-9251-8920Zohair AL-AMEEN1https://orcid.org/0000-0003-3630-2134University of Mosul, College of Education for Pure Science, Department of Computer ScienceUniversity of Mosul, University of Mosul Presidency, Computer Center, ICT Research UnitMagnetic resonance imaging (MRI) is a key method for imaging human tissues and organs. The accuracy of medical diagnosis is greatly affected by the quality of MRI images. Sometimes, MRI images are obtained blurry due to various inevitable constraints related to the imaging equipment, which affects the detection of important features in the image. Several sharpening methods were introduced, but not all were successful in this task, as artifacts may be introduced, contrast may be changed, and high complexity may be involved. Thus, this paper introduces a guided-subsumed unsharp mask filter (GSUM) to improve the sharpness of MRI images. The GSUM utilizes an improved guided filter instead of the low-pass Gaussian filter and a dynamic sharpening parameter. The improved guided filter employs a hybrid procedure instead of the mean filter in the smoothing process and relies on an adaptive regularization parameter. The applied modifications eliminated the overshooting and halo effects of the original unsharp masking and the guided filter, resulting in better-quality images. The GSUM was tested with real-blurry MRI images, evaluated using three no-reference metrics, and compared with six other algorithms. The metric scores indicate that the proposed filter can surpass existing methods, as it produced better results with average readings of 24.2074 in PIQE, 0.6878 in BLUR, and 5.7944 in FISH. It also scored a fast computation time, averaging 0.3384 seconds. https://ph.pollub.pl/index.php/acs/article/view/6577Unsharp mask Guided filter Image sharpening MRI images |
| spellingShingle | Manar AL-ABAJI Zohair AL-AMEEN SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER Applied Computer Science Unsharp mask Guided filter Image sharpening MRI images |
| title | SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER |
| title_full | SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER |
| title_fullStr | SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER |
| title_full_unstemmed | SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER |
| title_short | SHARPNESS IMPROVEMENT OF MAGNETIC RESONANCE IMAGES USING A GUIDED-SUBSUMED UNSHARP MASK FILTER |
| title_sort | sharpness improvement of magnetic resonance images using a guided subsumed unsharp mask filter |
| topic | Unsharp mask Guided filter Image sharpening MRI images |
| url | https://ph.pollub.pl/index.php/acs/article/view/6577 |
| work_keys_str_mv | AT manaralabaji sharpnessimprovementofmagneticresonanceimagesusingaguidedsubsumedunsharpmaskfilter AT zohairalameen sharpnessimprovementofmagneticresonanceimagesusingaguidedsubsumedunsharpmaskfilter |