Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging
Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. T...
- Autores:
-
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Contreras, Yudith
Cuberos, María
Vivas, Marisela
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Hernández, Carlos
Barrera, Doris
Molina, Ángel Valentín
Martínez, Luis Javier
Salazar, Juan
Gelvez, Elkin
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:20.500.12442/2525
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2525
- Palabra clave:
- Magnetic resonance brain imaging
Cerebral tumor
Low grade glioma
Grade II astrocytoma
Computational technique
Segmentation
Imágenes cerebrales por resonancia magnética
Tumor cerebral
Gliomas de bajo grado
Astrocitoma de grado II
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 |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| dc.title.alternative.spa.fl_str_mv |
Segmentación de glioma de bajo grado usando una técnica computacional automática en imágenes de resonancia magnética |
| title |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| spellingShingle |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging Magnetic resonance brain imaging Cerebral tumor Low grade glioma Grade II astrocytoma Computational technique Segmentation Imágenes cerebrales por resonancia magnética Tumor cerebral Gliomas de bajo grado Astrocitoma de grado II Técnica computacional Segmentación |
| title_short |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| title_full |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| title_fullStr |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| title_full_unstemmed |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| title_sort |
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging |
| dc.creator.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Contreras, Yudith Cuberos, María Vivas, Marisela Salazar, Williams Vera, María Isabel Borrero, Maryury Hernández, Carlos Barrera, Doris Molina, Ángel Valentín Martínez, Luis Javier Salazar, Juan Gelvez, Elkin Sáenz, Frank |
| dc.contributor.author.none.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Contreras, Yudith Cuberos, María Vivas, Marisela Salazar, Williams Vera, María Isabel Borrero, Maryury Hernández, Carlos Barrera, Doris Molina, Ángel Valentín Martínez, Luis Javier Salazar, Juan Gelvez, Elkin Sáenz, Frank |
| dc.subject.eng.fl_str_mv |
Magnetic resonance brain imaging Cerebral tumor Low grade glioma Grade II astrocytoma Computational technique Segmentation |
| topic |
Magnetic resonance brain imaging Cerebral tumor Low grade glioma Grade II astrocytoma Computational technique Segmentation Imágenes cerebrales por resonancia magnética Tumor cerebral Gliomas de bajo grado Astrocitoma de grado II Técnica computacional Segmentación |
| dc.subject.spa.fl_str_mv |
Imágenes cerebrales por resonancia magnética Tumor cerebral Gliomas de bajo grado Astrocitoma de grado II Técnica computacional Segmentación |
| description |
Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and postprocessing. The percent relative error (PrE) is considered to compare the segmentations of the LGG, generated by a neuro- oncologist manually, with the dilated segmentations of the LGG, obtained automatically. The combination of parameters linked to the lowest PrE, allow establishing the optimal parameters of each computational algorithm that makes up the proposed computational technique. The results allow reporting a PrE of 1.43%, which indicates 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 |
2019-01-25T15:15:04Z |
| dc.date.available.none.fl_str_mv |
2019-01-25T15:15:04Z |
| 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/20.500.12442/2525 |
| identifier_str_mv |
26107988 |
| url |
http://hdl.handle.net/20.500.12442/2525 |
| 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_2018/7%20_low_grade_glioma_segmentation.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-ff4614f93578Valbuena, Oscar262b3f8e-b422-4786-b036-2aaa5b963f84Contreras, Yudith5ec79ce9-bc7e-44bb-95cb-bf1dab3e3a64Cuberos, María8c8b8bcd-088d-4518-82f4-d7b4038e6a4bVivas, Mariselafce67a67-3a3b-493c-8fed-422fb00a2e71Salazar, 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-e70d635e8807Molina, Ángel Valentín5fcd607f-8710-40a9-b4dc-b9d1f71d1c1eMartínez, Luis Javierd0fa0a36-7752-496a-979e-48fdb02a5ee9Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181Gelvez, Elkin90dd023c-1cb7-48ef-bff5-4071ee82a94cSáenz, Frank5a93b50c-3ebe-476e-8aa6-4286185e2b1d2019-01-25T15:15:04Z2019-01-25T15:15:04Z201826107988http://hdl.handle.net/20.500.12442/2525Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and postprocessing. The percent relative error (PrE) is considered to compare the segmentations of the LGG, generated by a neuro- oncologist manually, with the dilated segmentations of the LGG, obtained automatically. The combination of parameters linked to the lowest PrE, allow establishing the optimal parameters of each computational algorithm that makes up the proposed computational technique. The results allow reporting a PrE of 1.43%, which indicates an excellent correlation between the manual segmentations and those produced by the computational technique developed.Por medio de este trabajo proponemos una técnica computacional para la segmentación de un tumor cerebral, identificado como glioma de bajo grado (LGG), específicamente astrocitoma de grado II, que está presente en imágenes de resonancia magnética (MRI). Esta técnica consiste en 3 etapas desarrolladas en el dominio tridimensional. Ellos son: pre procesamiento, segmentación y post procesamiento. El porcentaje de error relativo (PrE) se considera para comparar las segmentaciones de la LGG, generadas por un neurooncólogo de forma manual, con las segmentaciones dilatadas de la LGG, obtenidas automáticamente. La combinación de parámetros vinculados al PrE más bajo permite establecer los parámetros óptimos de cada algoritmo computacional que compone la técnica computacional propuesta. Los resultados permiten informar un PrE de 1.43%, lo que indica 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_2018/7%20_low_grade_glioma_segmentation.pdfMagnetic resonance brain imagingCerebral tumorLow grade gliomaGrade II astrocytomaComputational techniqueSegmentationImágenes cerebrales por resonancia magnéticaTumor cerebralGliomas de bajo gradoAstrocitoma de grado IITécnica computacionalSegmentaciónLow grade glioma segmentation using an automatic computational technique in magnetic resonance imagingSegmentación de glioma de bajo grado usando una técnica computacional automática 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 K. 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.Wu W., Lamborn K., Buckner J., Novotny P., Chang S., O’Fallon J., Jaeckle K., Prados M. Joint NCCTG and NABTC prognostic factors analysis for high-grade recurrent glioma. Neuro-oncology, 2010;12(2):164-172.Bjoern H. Menze et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 2015; 34(10):1993-2024.Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, et al. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008-2012. Neuro Oncol 2015 Oct;17 Suppl 4:iv1-iv62 PubMed ID 26511214.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.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.Cho H., Park H. (2017). Classification of low-grade and high-grade glioma using multi-modal image radiomics features. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 3081 – 3084.Sezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004; 13(1):146–165.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; 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, 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;193–200.Vera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D Thesis, Universidad de los Andes, Mérida-Venezuela, 2014.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf774374https://bonga.unisimon.edu.co/bitstreams/c76ac81e-d1a9-44d6-9c9f-f485d30e9959/downloadc0af4075cbeb48ef2e26219c9d28fc9cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/94e12b1a-ca9f-4319-8ebf-07501953d6b6/download3fdc7b41651299350522650338f5754dMD52TEXTLow grade glioma segmentation.pdf.txtLow grade glioma segmentation.pdf.txtExtracted texttext/plain31141https://bonga.unisimon.edu.co/bitstreams/9f6d0f0e-6391-4393-8659-4a78bf826955/download2a26ec3521a93863e725e9707a3c9868MD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain31599https://bonga.unisimon.edu.co/bitstreams/6b7559b4-5663-4346-ad01-4124f489b6ee/downloadb39f4db2303636093f01e436c1895539MD55THUMBNAILLow grade glioma segmentation.pdf.jpgLow grade glioma segmentation.pdf.jpgGenerated Thumbnailimage/jpeg2070https://bonga.unisimon.edu.co/bitstreams/df37a1a9-57f0-4a28-8020-2ab24e14d401/downloadbcec5993e5171eeeaabd4ca101a6bc1aMD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg7142https://bonga.unisimon.edu.co/bitstreams/1ffa2aa2-8303-45ce-ba93-98b2e0680770/download7b67883ab9ef576528102aa495a2d580MD5620.500.12442/2525oai:bonga.unisimon.edu.co:20.500.12442/25252024-08-14 21:52:42.911open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4= |