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
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dc.title.eng.fl_str_mv Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
dc.title.alternative.spa.fl_str_mv Filtros suavizadores en imágenes sintéticas de resonancia magnética cerebral: un estudio comparativo
title Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
spellingShingle Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
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
title_short Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
title_full Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
title_fullStr Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
title_full_unstemmed Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
title_sort Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
dc.creator.fl_str_mv 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
dc.contributor.author.none.fl_str_mv 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
dc.subject.eng.fl_str_mv Synthetic Cerebral images
Magnetic resonance
Rician noise
Gaussian filter
Anisotropic diffusion filter
topic 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
dc.subject.spa.fl_str_mv PSNR
Imágenes sintéticas cerebrales
Resonancia magnética
Ruido Riciano
Filtro Gausiano
Filtro de difusión anisotrópica
description 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.
publishDate 2018
dc.date.issued.none.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2019-01-25T19:23:30Z
dc.date.available.none.fl_str_mv 2019-01-25T19:23:30Z
dc.type.eng.fl_str_mv article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.identifier.issn.none.fl_str_mv 18564550
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12442/2529
identifier_str_mv 18564550
url http://hdl.handle.net/20.500.12442/2529
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
rights_invalid_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
dc.publisher.spa.fl_str_mv Sociedad Latinoamericana de Hipertensión
dc.source.spa.fl_str_mv Revista Latinoamericana de Hipertensión
Vol. 13, No. 4 (2018)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv http://www.revhipertension.com/rlh_4_2018/5_smoothing_filters_synthetic.pdf
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spelling Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Gelvez, Elkin90dd023c-1cb7-48ef-bff5-4071ee82a94cVera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e3Huérfano, Yoleidy769899ba-e6a1-4144-95c2-ff4614f93578Valbuena, Oscar262b3f8e-b422-4786-b036-2aaa5b963f84Salazar, Williamsfd007214-08c4-4cd6-ae19-7f2ba4f184eaVera, María Isabelc522f56e-ec03-4aa6-9e83-d339a37388acBorrero, Maryuryce8424b3-6f43-4a46-8f73-214fafbb62fdBarrera, Doris4b365c16-7d6f-4aee-985c-e70d635e8807Hernández, Carlosa82d5fb1-0724-456f-8223-93882ad7278dMolina, Ángel Valentín5fcd607f-8710-40a9-b4dc-b9d1f71d1c1eMartínez, Luis Javierd0fa0a36-7752-496a-979e-48fdb02a5ee9Sáenz, Frank5a93b50c-3ebe-476e-8aa6-4286185e2b1dVivas, Mariselafce67a67-3a3b-493c-8fed-422fb00a2e71Contreras, Julioee6833d4-08fc-4f10-8638-cd5206d5aeddRestrepo, Jorge7b931d24-b676-443a-a392-6306df154e75Vanegas, Juan99f4bb28-f65b-4949-9493-cf2b3b35984bSalazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181Contreras, Yudith5ec79ce9-bc7e-44bb-95cb-bf1dab3e3a642019-01-25T19:23:30Z2019-01-25T19:23:30Z201818564550http://hdl.handle.net/20.500.12442/2529This 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.Este artículo presenta la evaluación de dos técnicas computacionales para el suavizado de ruido, que puede estar presente en imágenes sintéticas o phantoms numéricos de resonancia magnética (MRI). Las imágenes que servirán como bases de datos (DB) para el desarrollo de la mencionada evaluación están disponibles, de manera libre, en la Internet y se reportan, en la literatura especializada, como imágenes sintéticas denominadas BrainWeb. Las imágenes pertenecientes a esta DB fueron contaminadas con ruido Riciano debido a que este es el tipo de ruido más frecuente en imágenes de MRI reales. Por otra parte, las técnicas consideradas para minimizar el impacto de este ruido, en la calidad de las imágenes de la BrainWeb, se hacen coincidir con el filtro Gausiano (GF) y un filtro de difusión anisotrópica, basado en el gradiente de la imagen (GADF). Cada uno de estos filtros posee 2 parámetros que controlan su funcionamiento y, por ende, deben someterse a un proceso de entonación riguroso para identificar los valores óptimos que garanticen el mejor desempeño tanto del GF como del GADF. El pico de la relación señal a ruido (PSNR) y el tiempo de cómputo son considerados como elementos clave para analizar el comportamiento de cada una de las técnicas de filtrado aplicadas. Los resultados indican que: a) Ambos filtros generan valores de PSNR comparables entre sí. b) El GF requiere de un tiempo de cómputo, significativamente, menor para suavizar el ruido Riciano presente en la DB considerada.engSociedad Latinoamericana de HipertensiónRevista Latinoamericana de HipertensiónVol. 13, No. 4 (2018)http://www.revhipertension.com/rlh_4_2018/5_smoothing_filters_synthetic.pdfSynthetic Cerebral imagesMagnetic resonanceRician noiseGaussian filterAnisotropic diffusion filterPSNRImágenes sintéticas cerebralesResonancia magnéticaRuido RicianoFiltro GausianoFiltro de difusión anisotrópicaSmoothing filters in synthetic cerebral magnetic resonance images: A comparative studyFiltros suavizadores en imágenes sintéticas de resonancia magnética cerebral: un estudio comparativoarticlehttp://purl.org/coar/resource_type/c_6501Gudbjartsson H. y Patz S.The rician distribution of noisy MRI data, Magn. Reson. Med. 34 (1) (1995) 910914.Macovski A. (1996). Noise in MRI, Magn. Reson. Med. 36 (1) 494497.Cocosco C., Kollokian V., Kwan R. y Evans A. (1997). BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. NeuroImage, 5(4), part 2/4, S425, 1997. Proceedings of 3-rd International Conference on Functional Mapping of the Human Brain, Copenhagen.Kwan R., Evans A. y Pike G. (1999). MRI simulation-based evaluation of image processing and classification methods. IEEE Transactions on Medical Imaging. 18(11):1085-97.Collins D., Zijdenbos A., Kollokian V., Sled J. y Kabani N., Holmes C., Evans A. (1998). Design and Construction of a Realistic Digital Brain Phantom. IEEE Transactions on Medical Imaging, 17(3):463-468.Kwan R., Evans A. y Pike G. (1996). An Extensible MRI Simulator for Post-Processing-Evaluation-Visualization in Biomedical Computing (VBC’96). Lecture Notes in Computer Science, 1131:135-140.Springer- Verlag,Coupé P., Yger P., Prima S., Hellier P., Kervrann C. y Barillot C. (2008). An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images, IEEE Trans. Med. Imag. 27(4):425–441.Perona P. y Malik J. (1990). Scalespace and edge detection using anisotropic diffusion, IEEE Trans. on Patt. Analysis and Machine Intelligence 12(7): 629–639.Vera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D. dissertation, Universidad de los Andes, Mérida-Venezuela, 2014.Meijering H. Image enhancement in digital X–ray angiography. [Tesis Doctoral], Utrecht University, Netherlands, 2000.González R., Woods R. Digital Image Processing. USA: Prentice Hall, 2001.Pratt W. Digital Image Processing. USA: John Wiley & Sons Inc, 2007.Netravali A. y Haskell B. Digital Pictures: Representation, Compression, and Standards (2nd Ed), Plenum Press, New York, NY (1995).Rabbani M. y Jones P. Digital Image Compression Techniques, Vol TT7, SPIE Optical Engineering Press, Bellvue, Washington (1991).Vera M., Huérfano Y., Contreras J., Vera M. I., Salazar W., Vargas S., Chacón J. y Rodríguez J. (2017). Detección de hemorragia intracraneal intraparenquimatosa, en imágenes de tomografía computarizada cerebral, usando una técnica computacional no lineal. Latinoamericana de Hipertensión. 12(5), 125-130.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf327340https://bonga.unisimon.edu.co/bitstreams/114fff98-c06d-4161-9632-93f0c59afcdf/downloadd5ab7f8c4aac01ae808d472d6ae2e2a2MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/0625f8be-d435-4720-937c-588262560d17/download3fdc7b41651299350522650338f5754dMD52TEXTSmoothing filters in synthetic.pdf.txtSmoothing filters in synthetic.pdf.txtExtracted texttext/plain20933https://bonga.unisimon.edu.co/bitstreams/5592b65c-5b9b-42e9-b6ae-54a08b4b0f43/downloadb1300a1f21227f13659f8d2de55f1657MD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain21182https://bonga.unisimon.edu.co/bitstreams/53ff6e02-4afe-47df-a954-e973dee1937e/download2ce6b606492ec1fb2724b819169be5e5MD55THUMBNAILSmoothing filters in synthetic.pdf.jpgSmoothing filters in synthetic.pdf.jpgGenerated Thumbnailimage/jpeg1873https://bonga.unisimon.edu.co/bitstreams/fe595934-8864-4810-b1ce-cc8cac8b4069/download314d8c7ae68c97419a6a3726d149fb94MD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg6310https://bonga.unisimon.edu.co/bitstreams/faae7c1b-5c1a-4b71-8f5e-9f62b820f878/downloadb5d36c48a5f6ffd789b3b5ac1a5aca9aMD5620.500.12442/2529oai:bonga.unisimon.edu.co:20.500.12442/25292024-08-14 21:53:17.755open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4=