Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
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| Format: | Artículo científico |
| Sprache: | en |
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Sociedad Latinoamericana de Hipertensión
2018
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| _version_ | 1876457819983577088 |
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| author | Elkin Gelvez |
| author_facet | Elkin Gelvez |
| contents | Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study Elkin Gelvez Miguel Vera Yoleidy Huérfano Oscar Valbuena Williams Salazar María Isabel Vera Maryury Borrero Doris Barrera Carlos Hernández Ángel Valentín Molina Luis Javier Martínez Frank Sáenz Marisela Vivas Julio Contreras Jorge Restrepo Juan Vanegas Juan Salazar Yudith Contreras Medicina PSNR Rician noise Gaussian filter Magnetic resonance Synthetic Cerebral images This paper presents the evaluation of two computational techniques for smoothing noise that might be present in synthetic images or numerical phantoms of magnetic resonance (MRI). The images that will serve as the databases (DB) during the course of this evaluation are available freely on the Internet and are reported in specialized literature as synthetic images called BrainWeb. The images that belong to this DB were contaminated with Rician noise, this being the most frequent type of noise in real MRI images. Also, the techniques that are usually considered to minimize the impact of Rician noise on the quality of BrainWeb images are matched with the Gaussian filter (GF) and an anisotropic diffusion filter, based on the gradient of the image (GADF). Each of these filters has 2 parameters that control their operation and, therefore, undergo a rigorous tuning process to identify the optimal values that guarantee the best performance of both the GF and the GADF. The peak of the signal-to-noise ratio (PSNR) and the computation time are considered as key elements to analyze the behavior of each of the filtering techniques applied. The results indicate that: a) both filters generate PSNR values comparable to each other. b) The GF requires a significantly shorter computation time to soften the Rician noise present in the considered DB. 2018 artículo científico 1856-4550 https://www.redalyc.org/articulo.oa?id=170263336005 https://www.redalyc.org/journal/1702/170263336005/ https://www.redalyc.org/journal/1702/170263336005/html/ https://www.redalyc.org/journal/1702/170263336005/170263336005.epub https://www.redalyc.org/journal/1702/170263336005/movil en http://www.redalyc.org/revista.oa?id=1702 Revista Latinoamericana de Hipertensión application/pdf Sociedad Latinoamericana de Hipertensión Revista Latinoamericana de Hipertensión (República Bolivariana de Venezuela) Num.4 Vol.13 |
| format | Artículo científico |
| id | redalyc_170263336005 |
| institution | Redalyc |
| language | en |
| publishDate | 2018 |
| publisher | Sociedad Latinoamericana de Hipertensión |
| spellingShingle | Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study Elkin Gelvez Medicina PSNR Rician noise Gaussian filter Magnetic resonance Synthetic Cerebral images Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study Elkin Gelvez Miguel Vera Yoleidy Huérfano Oscar Valbuena Williams Salazar María Isabel Vera Maryury Borrero Doris Barrera Carlos Hernández Ángel Valentín Molina Luis Javier Martínez Frank Sáenz Marisela Vivas Julio Contreras Jorge Restrepo Juan Vanegas Juan Salazar Yudith Contreras Medicina PSNR Rician noise Gaussian filter Magnetic resonance Synthetic Cerebral images This paper presents the evaluation of two computational techniques for smoothing noise that might be present in synthetic images or numerical phantoms of magnetic resonance (MRI). The images that will serve as the databases (DB) during the course of this evaluation are available freely on the Internet and are reported in specialized literature as synthetic images called BrainWeb. The images that belong to this DB were contaminated with Rician noise, this being the most frequent type of noise in real MRI images. Also, the techniques that are usually considered to minimize the impact of Rician noise on the quality of BrainWeb images are matched with the Gaussian filter (GF) and an anisotropic diffusion filter, based on the gradient of the image (GADF). Each of these filters has 2 parameters that control their operation and, therefore, undergo a rigorous tuning process to identify the optimal values that guarantee the best performance of both the GF and the GADF. The peak of the signal-to-noise ratio (PSNR) and the computation time are considered as key elements to analyze the behavior of each of the filtering techniques applied. The results indicate that: a) both filters generate PSNR values comparable to each other. b) The GF requires a significantly shorter computation time to soften the Rician noise present in the considered DB. 2018 artículo científico 1856-4550 https://www.redalyc.org/articulo.oa?id=170263336005 https://www.redalyc.org/journal/1702/170263336005/ https://www.redalyc.org/journal/1702/170263336005/html/ https://www.redalyc.org/journal/1702/170263336005/170263336005.epub https://www.redalyc.org/journal/1702/170263336005/movil en http://www.redalyc.org/revista.oa?id=1702 Revista Latinoamericana de Hipertensión application/pdf Sociedad Latinoamericana de Hipertensión Revista Latinoamericana de Hipertensión (República Bolivariana de Venezuela) Num.4 Vol.13 |
| title | Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study |
| topic | Medicina PSNR Rician noise Gaussian filter Magnetic resonance Synthetic Cerebral images |
| url | https://www.redalyc.org/articulo.oa?id=170263336005 https://www.redalyc.org/journal/1702/170263336005/ https://www.redalyc.org/journal/1702/170263336005/html/ https://www.redalyc.org/journal/1702/170263336005/170263336005.epub https://www.redalyc.org/journal/1702/170263336005/movil |