Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising
Evaluating the results of image denoising algorithms in Computed Tomography (CT) scans typically involves several key metrics to assess noise reduction while preserving essential details. Full Reference (FR) quality evaluators are popular for evaluating image quality in denoising CT scans. There is...
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MDPI AG
2025-01-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/1/18 |
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| author | Rudy Gunawan Yvonne Tran Jinchuan Zheng Hung Nguyen Rifai Chai |
| author_facet | Rudy Gunawan Yvonne Tran Jinchuan Zheng Hung Nguyen Rifai Chai |
| author_sort | Rudy Gunawan |
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| description | Evaluating the results of image denoising algorithms in Computed Tomography (CT) scans typically involves several key metrics to assess noise reduction while preserving essential details. Full Reference (FR) quality evaluators are popular for evaluating image quality in denoising CT scans. There is limited information about using Blind/No Reference (NR) quality evaluators in the medical image area. This paper shows the previously utilized Natural Image Quality Evaluator (NIQE) in CT scans; this NIQE is commonly used as a photolike image evaluator and provides an extensive assessment of the optimum NIQE setting. The result was obtained using the library of good images. Most are also part of the Convolutional Neural Network (CNN) training dataset against the testing dataset, and a new dataset shows an optimum patch size and contrast levels suitable for the task. This evidence indicates a possibility of using the NIQE as a new option in evaluating denoised quality to find improvement or compare the quality between CNN models. |
| format | Article |
| id | doaj-art-9843d83718b44a95beed4740c6368b16 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-9843d83718b44a95beed4740c6368b162025-01-24T13:27:53ZengMDPI AGComputers2073-431X2025-01-011411810.3390/computers14010018Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan DenoisingRudy Gunawan0Yvonne Tran1Jinchuan Zheng2Hung Nguyen3Rifai Chai4School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaMacquarie University Hearing (MU Hearing), Centre for Healthcare Resilience and Implementation Science, Macquarie University, Macquarie Park, Sydney, NSW 2109, AustraliaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaEvaluating the results of image denoising algorithms in Computed Tomography (CT) scans typically involves several key metrics to assess noise reduction while preserving essential details. Full Reference (FR) quality evaluators are popular for evaluating image quality in denoising CT scans. There is limited information about using Blind/No Reference (NR) quality evaluators in the medical image area. This paper shows the previously utilized Natural Image Quality Evaluator (NIQE) in CT scans; this NIQE is commonly used as a photolike image evaluator and provides an extensive assessment of the optimum NIQE setting. The result was obtained using the library of good images. Most are also part of the Convolutional Neural Network (CNN) training dataset against the testing dataset, and a new dataset shows an optimum patch size and contrast levels suitable for the task. This evidence indicates a possibility of using the NIQE as a new option in evaluating denoised quality to find improvement or compare the quality between CNN models.https://www.mdpi.com/2073-431X/14/1/18CT scanneural networkdenoisingBlind evaluatorreference less evaluatorNIQE |
| spellingShingle | Rudy Gunawan Yvonne Tran Jinchuan Zheng Hung Nguyen Rifai Chai Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising Computers CT scan neural network denoising Blind evaluator reference less evaluator NIQE |
| title | Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising |
| title_full | Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising |
| title_fullStr | Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising |
| title_full_unstemmed | Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising |
| title_short | Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising |
| title_sort | optimizing natural image quality evaluators for quality measurement in ct scan denoising |
| topic | CT scan neural network denoising Blind evaluator reference less evaluator NIQE |
| url | https://www.mdpi.com/2073-431X/14/1/18 |
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