Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging
Through this work we propose a computational technique for the segmentation of a brain tumor, identified as meningioma (MGT), which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain: pre-processing, segmentation and post-proc...
- Autores:
-
Vera, Miguel
Huérfano, Yoleidy
Molina, Ángel Valentín
Valbuena, Oscar
Vivas, Marisela
Cuberos, María
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Hernández, Carlos
Barrera, Doris
Martínez, Luis Javier
Salazar, Juan
Gelvez, Elkin
Contreras, Yudith
Sáenz, Frank
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2521
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2521
- Palabra clave:
- Magnetic resonance brain imaging
Brain tumor
Meningioma
Computational technique
Segmentation
Imágenes cerebrales por resonancia magnética
Tumor cerebral
Meningioma
Técnica computacional
Segmentación
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| dc.title.alternative.spa.fl_str_mv |
Segmentación automática de un meningioma usando una técnica computacional en imágenes de resonancia magnética |
| title |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| spellingShingle |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging Magnetic resonance brain imaging Brain tumor Meningioma Computational technique Segmentation Imágenes cerebrales por resonancia magnética Tumor cerebral Meningioma Técnica computacional Segmentación |
| title_short |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| title_full |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| title_fullStr |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| title_full_unstemmed |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| title_sort |
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging |
| dc.creator.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Molina, Ángel Valentín Valbuena, Oscar Vivas, Marisela Cuberos, María Salazar, Williams Vera, María Isabel Borrero, Maryury Hernández, Carlos Barrera, Doris Martínez, Luis Javier Salazar, Juan Gelvez, Elkin Contreras, Yudith Sáenz, Frank |
| dc.contributor.author.none.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Molina, Ángel Valentín Valbuena, Oscar Vivas, Marisela Cuberos, María Salazar, Williams Vera, María Isabel Borrero, Maryury Hernández, Carlos Barrera, Doris Martínez, Luis Javier Salazar, Juan Gelvez, Elkin Contreras, Yudith Sáenz, Frank |
| dc.subject.eng.fl_str_mv |
Magnetic resonance brain imaging Brain tumor Meningioma Computational technique |
| topic |
Magnetic resonance brain imaging Brain tumor Meningioma Computational technique Segmentation Imágenes cerebrales por resonancia magnética Tumor cerebral Meningioma Técnica computacional Segmentación |
| dc.subject.spa.fl_str_mv |
Segmentation Imágenes cerebrales por resonancia magnética Tumor cerebral Meningioma Técnica computacional Segmentación |
| description |
Through this work we propose a computational technique for the segmentation of a brain tumor, identified as meningioma (MGT), which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain: pre-processing, segmentation and post-processing. The percent relative error (PrE) is considered to compare the segmentations of the MGT, generated by a neuro-oncologist manually, with the dilated segmentations of the MGT, obtained automatically. The combination of parameters linked to the lowest PrE, provides the optimal parameters of each computational algorithm that makes up the proposed computational technique. Results allow reporting a PrE of 1.44%, showing an excellent correlation between the manual segmentations and those produced by the computational technique developed. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2019-01-24T22:46:58Z |
| dc.date.available.none.fl_str_mv |
2019-01-24T22:46:58Z |
| 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 |
26107988 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12442/2521 |
| identifier_str_mv |
26107988 |
| url |
http://hdl.handle.net/20.500.12442/2521 |
| dc.language.iso.eng.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional |
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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 Venezolana de Farmacología Clínica y Terapéutica |
| dc.source.spa.fl_str_mv |
Revista AVFT-Archivos Venezolanos de Farmacología y Terapéutica Vol. 37, No. 4 (2018) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.eng.fl_str_mv |
http://www.revistaavft.com/images/revistas/2018/avft_4_2018/6_automatic_segmentation_of_a_meningioma.pdf |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Vera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e3Huérfano, Yoleidy769899ba-e6a1-4144-95c2-ff4614f93578Molina, Ángel Valentín5fcd607f-8710-40a9-b4dc-b9d1f71d1c1eValbuena, Oscar262b3f8e-b422-4786-b036-2aaa5b963f84Vivas, Marisela8a48d7f3-9b15-4821-892c-91f9749f1286Cuberos, María8c8b8bcd-088d-4518-82f4-d7b4038e6a4bSalazar, Williamsfd007214-08c4-4cd6-ae19-7f2ba4f184eaVera, María Isabelc522f56e-ec03-4aa6-9e83-d339a37388acBorrero, Maryuryce8424b3-6f43-4a46-8f73-214fafbb62fdHernández, Carlosa82d5fb1-0724-456f-8223-93882ad7278dBarrera, Doris4b365c16-7d6f-4aee-985c-e70d635e8807Martínez, Luis Javierd0fa0a36-7752-496a-979e-48fdb02a5ee9Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181Gelvez, Elkin90dd023c-1cb7-48ef-bff5-4071ee82a94cContreras, Yudith5ec79ce9-bc7e-44bb-95cb-bf1dab3e3a64Sáenz, Frank5a93b50c-3ebe-476e-8aa6-4286185e2b1d2019-01-24T22:46:58Z2019-01-24T22:46:58Z201826107988http://hdl.handle.net/20.500.12442/2521Through this work we propose a computational technique for the segmentation of a brain tumor, identified as meningioma (MGT), which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain: pre-processing, segmentation and post-processing. The percent relative error (PrE) is considered to compare the segmentations of the MGT, generated by a neuro-oncologist manually, with the dilated segmentations of the MGT, obtained automatically. The combination of parameters linked to the lowest PrE, provides the optimal parameters of each computational algorithm that makes up the proposed computational technique. Results allow reporting a PrE of 1.44%, showing an excellent correlation between the manual segmentations and those produced by the computational technique developed.Este trabajo propone una técnica computacional para la segmentación de un tumor cerebral, identificado como meningioma (MGT), que está presente en imágenes de resonancia magnética (MRI). Esta técnica consta de 3 etapas desarrolladas en el dominio tridimensional: preprocesamiento, segmentación y postprocesamiento. El porcentaje de error relativo (PrE) se considera para comparar las segmentaciones de la MGT, generadas por un neurooncólogo de forma manual, con las segmentaciones dilatadas de la MGT, obtenidas automáticamente. La combinación de parámetros vinculados al PrE más bajo proporciona los parámetros óptimos de cada algoritmo computacional que conforma la técnica de cálculo propuesta. Los resultados permiten informar un PrE de 1.44%, mostrando una excelente correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada.engSociedad Venezolana de Farmacología Clínica y TerapéuticaRevista AVFT-Archivos Venezolanos de Farmacología y TerapéuticaVol. 37, No. 4 (2018)http://www.revistaavft.com/images/revistas/2018/avft_4_2018/6_automatic_segmentation_of_a_meningioma.pdfMagnetic resonance brain imagingBrain tumorMeningiomaComputational techniqueSegmentationImágenes cerebrales por resonancia magnéticaTumor cerebralMeningiomaTécnica computacionalSegmentaciónAutomatic segmentation of a meningioma using a computational technique in magnetic resonance imagingSegmentación automática de un meningioma usando una técnica computacional en imágenes de resonancia magnéticaarticlehttp://purl.org/coar/resource_type/c_6501Stelzer K. Epidemiology and prognosis of brain metastases. Surg Neurol Int. 2013;4(Suppl 4):S192-202.Mcneill KA. Epidemiology of Brain Tumors. Neurol Clin. 2016;34(4):981-998.American Brain Tumor Association (ABTA). About Brain Tumors: A Primer for Patients and Caregivers. 9ª Edition. 2015 ABTA.WHO (2007). Cavenee W, Louis D, Ohgaki H et al. Eds. WHO Classification of Tumours of the Central Nervous System. WHO Regional Office Europe.Burger, Scheithauer, and Vogel, Surgical Pathology of the Nervous System and Its Coverings. 4th edition. Churchill Livingstone, Nueva York, 2002.Gudbjartsson H. y Patz S.The rician distribution of noisy MRI data, Magn. Reson. Med. 1995:34 (1):910-914.Macovski A. Noise in MRI, Magn. Reson. Med. 1996:36 (1) 494-497.Sanjuán A., Price C., Mancini L., Josse G., Grogan A., Yamamoto A., Geva S., Leff A., Yousry T., Seghier M. Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Frontiers in Neuroscience. 2013:7(1):241-257Hsieh T., Liu Y., Liao C., Xiao F., Chiang I., Wong J. Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing. BMC Medical Informatics and Decision Making. 2011:11(1): 11-54.Kaus M., Warfield S., Nabavi A., Chatzidakis E., Black P., Jolesz F. (1999). Segmentation of Meningiomas and Low Grade Gliomas in MRI. In Proceedings of Medical Image Computing and Computer-Assisted Intervention -- MICCAI’99. Kikinis R., Taylor, C. and Colchester A. editors. Springer Berlin Heidelberg. 1-10.Serra J. Image Analysis Using Mathematical Morphology. London, England: Academic Press, 1982.González R., Woods R. Digital Image Processing. USA: Prentice Hall, 2001.Mukhopadhyay S., Chanda B. A multiscale morphological approach to local contrast enhancement. Signal Processing. 2000: 80(4): 685– 696.Yu Z., Wei G., Zhen C., Jing T., Ling L. Medical images edge detection based on mathematical morphology. In Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai– China, September 2005, pp. 6492–6495.W. Pratt. Digital Image Processing. USA: John Wiley & Sons Inc, 2007.Fischer M., Paredes J., Arce G. Weighted median image sharpeners for the world wide web. IEEE Transactions on Image Processing. 2002:11(7):717-27.V. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons, 1998.E. Osuna, R. Freund, y F. Girosi. Training support vector machines: an application to face detection. In Conference on Computer Vision and Pattern Recognition (CVPR ’97), San Juan, Puerto Rico, 1997, pp. 130–136.A. Smola, “Learning with kernels,” Ph.D Thesis, Technische Universitt Berlin,Germany, 1998.B. Scholkopf y A. Smola, Learning with Kernels: Support Vector Machines, Regularization,Optimization, and Beyond. Cambridge, MA , USA: The MIT Press, 2002.J. Suykens, T. V. Gestel, y J. D. Brabanter, Least Squares Support Vector Machines.UK: World Scientific Publishing Co., 2002.M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, y T. Poggio. Pedestrian detection using wavelet templates. In CVPR ’97: Conference on Computer Vision and Pattern Recognition (CVPR ’97). Washington, DC, USA: IEEE Computer Society, 1997, pp. 193–200.Vera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D. dissertation, Universidad de los Andes, Mérida-Venezuela, 2014.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf741377https://bonga.unisimon.edu.co/bitstreams/197f6a9f-7f01-4e2a-ac7e-53ca45da4523/download3ff85a08c2c828bb739676cbe2532ffeMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/7fdde15c-f0ba-4c7f-ae17-1802870e9abd/download3fdc7b41651299350522650338f5754dMD52TEXTAutomatic segmentation.pdf.txtAutomatic segmentation.pdf.txtExtracted texttext/plain29472https://bonga.unisimon.edu.co/bitstreams/6942a067-408d-47a2-9742-7f18f1611157/downloadc10de23f5af2bdab78f953452167dccdMD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain29950https://bonga.unisimon.edu.co/bitstreams/a6a7aaec-e90a-4412-826f-b2bffed19f2e/download97d623503c3a5d6399aa7b5002746b62MD55THUMBNAILAutomatic segmentation.pdf.jpgAutomatic segmentation.pdf.jpgGenerated Thumbnailimage/jpeg2093https://bonga.unisimon.edu.co/bitstreams/e5169245-ff97-4941-878d-58cd087564c9/download5bed1389a4fe4f72275fcf87ba97c0b2MD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg7097https://bonga.unisimon.edu.co/bitstreams/73e8030b-b648-4cb0-b08e-f3a560a5ae7b/download23cf069566eafc309d39125d1eb713ffMD5620.500.12442/2521oai:bonga.unisimon.edu.co:20.500.12442/25212024-08-14 21:54:14.435open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4= |