ArticleJournal of biomedical physics & engineering2020
Noise Reduction in CT Images Using a Selective Mean Filter.
Article in Journal of biomedical physics & engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image.Computational and mathematical methods in medicine · 2022Trial
- LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning.Journal of imaging informatics in medicine · 2024Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images.Journal of applied clinical medical physics · 2022Article
- Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques.Saudi journal of biological sciences · 2022Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundNoise reduction is a method for reducing CT dose; however, it can reduce image quality.
objectiveThis study aims to propose a selective mean filter (SMF) and evaluate its effectiveness for noise suppression in CT images. MATERIAL AND
methodsThis experimental study proposed and implemented the new noise reduction algorithm. The proposed algorithm is based on a mean filter (MF), but the calculation of the mean pixel value using the neighboring pixels in a kernel selectively applied a threshold value based on the noise of the image. The SMF method was evaluated using images of phantoms. The dose reduction was estimated by comparing the image noise acquired with a lower dose after implementing the SMF method and the noise in the original image acquired with a higher dose. For comparison, the images were also filtered with an adaptive mean filter (AMF) and a bilateral filter (BF).
resultsThe spatial resolution of the image filtered with the SMF was similar to the original images and the images filtered with the BF. While using the AMF, spatial resolution was significantly corrupted. The noise reduction achieved using the SMF was up to 75%, while it was up to 50% using the BF.
conclusionSMF significantly reduces the noise and preserves the spatial resolution of the image. The noise reduction was more pronounced with BF, and less pronounced with AMF.
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