Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study

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 Intern...

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Autores:
Gelvez, Elkin
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Barrera, Doris
Hernández, Carlos
Molina, Ángel Valentín
Martínez, Luis Javier
Sáenz, Frank
Vivas, Marisela
Contreras, Julio
Restrepo, Jorge
Vanegas, Juan
Salazar, Juan
Contreras, Yudith
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/2529
Acceso en línea:
http://hdl.handle.net/20.500.12442/2529
Palabra clave:
Synthetic Cerebral images
Magnetic resonance
Rician noise
Gaussian filter
Anisotropic diffusion filter
PSNR
Imágenes sintéticas cerebrales
Resonancia magnética
Ruido Riciano
Filtro Gausiano
Filtro de difusión anisotrópica
Rights
License
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
Description
Summary: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.