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The impact of improved non-local means denoising algorithm on photon-counting X-ray images using various Al additive filtrations.
- Source :
-
Nuclear Instruments & Methods in Physics Research Section A . Mar2022, Vol. 1027, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- The filters used in X-ray systems affect the image quality and radiation dose. Although using filters has advantages in terms of image quality and dose, image noise increases because the amount of X-rays in the energy range of the entire area is reduced. Thus, a newly improved non-local means (INLM) denoising algorithm was modeled and applied to X-ray images based on the thickness of the aluminum (Al) filter, which is most commonly used in X-ray imaging systems, to prove its potential use. For X-ray image acquisition, a high-performance cadmium telluride material-based detector and a tube containing Al filters (1-, 3-, and 5-mm thickness) were designed. The proposed INLM denoising algorithm was modeled by including the improved weights for the gradient information for each pixel in the conventional NLM-based equation. When the proposed INLM denoising algorithm was applied to the acquired photon-counting X-ray images, results showed superior performance for both noise characteristics and no-reference-based image evaluation. In particular, we confirmed that when the INLM was applied to X-ray images using 5 mm Al thickness, noise characteristics and no-reference-based evaluation results were improved by approximately 1.36 times and 1.06 times, respectively, compared to the conventional NLM. The study proved that choosing the INLM in photon-counting X-ray images using 5 mm-thick Al filtration is vital for the success of image processing applications. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01689002
- Volume :
- 1027
- Database :
- Academic Search Index
- Journal :
- Nuclear Instruments & Methods in Physics Research Section A
- Publication Type :
- Academic Journal
- Accession number :
- 155190270
- Full Text :
- https://doi.org/10.1016/j.nima.2021.166244