Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis
Article
Ardakani, Ali Abbasian, Mohammadi, Afshin, Faeghi, Fariborz and Acharya, U. Rajendra. 2023. "Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis." International Journal of Imaging Systems and Technology. 33 (2), pp. 445-464. https://doi.org/10.1002/ima.22843
Article Title | Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis |
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ERA Journal ID | 36561 |
Article Category | Article |
Authors | Ardakani, Ali Abbasian, Mohammadi, Afshin, Faeghi, Fariborz and Acharya, U. Rajendra |
Journal Title | International Journal of Imaging Systems and Technology |
Journal Citation | 33 (2), pp. 445-464 |
Number of Pages | 20 |
Year | 2023 |
Publisher | John Wiley & Sons |
Place of Publication | United States |
ISSN | 0899-9457 |
1098-1098 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/ima.22843 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.22843 |
Abstract | Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full-reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full-reference and three no-reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second-best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre-processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies. |
Keywords | denoising filter; image denoising, noise reduction; Gaussian noise; ultrasound images; speckle noise; image denoising; noise reduction |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Shahid Beheshti University of Medical Sciences, Iran |
Urmia University of Medical Science, Iran | |
School of Mathematics, Physics and Computing | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v56/performance-evaluation-of-67-denoising-filters-in-ultrasound-images-a-systematic-comparison-analysis
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