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Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging

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dc.contributor.authorVera, Miguel
dc.contributor.authorHuérfano, Yoleidy
dc.contributor.authorValbuena, Oscar
dc.contributor.authorContreras, Yudith
dc.contributor.authorCuberos, María
dc.contributor.authorVivas, Marisela
dc.contributor.authorSalazar, Williams
dc.contributor.authorVera, María Isabel
dc.contributor.authorBorrero, Maryury
dc.contributor.authorHernández, Carlos
dc.contributor.authorBarrera, Doris
dc.contributor.authorMolina, Ángel Valentín
dc.contributor.authorMartínez, Luis Javier
dc.contributor.authorSalazar, Juan
dc.contributor.authorGelvez, Elkin
dc.contributor.authorSáenz, Frank
dc.date.accessioned2019-01-25T15:15:04Z
dc.date.available2019-01-25T15:15:04Z
dc.date.issued2018
dc.description.abstractThrough 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.eng
dc.description.abstractPor 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.spa
dc.identifier.issn26107988
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2525
dc.language.isoengeng
dc.publisherSociedad Venezolana de Farmacología Clínica y Terapéuticaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.sourceRevista AVFT-Archivos Venezolanos de Farmacología y Terapéuticaspa
dc.sourceVol. 37, No. 4 (2018)spa
dc.source.urihttp://www.revistaavft.com/images/revistas/2018/avft_4_2018/7%20_low_grade_glioma_segmentation.pdfeng
dc.subjectMagnetic resonance brain imagingeng
dc.subjectCerebral tumoreng
dc.subjectLow grade gliomaeng
dc.subjectGrade II astrocytomaeng
dc.subjectComputational techniqueeng
dc.subjectSegmentationeng
dc.subjectImágenes cerebrales por resonancia magnéticaspa
dc.subjectTumor cerebralspa
dc.subjectGliomas de bajo gradospa
dc.subjectAstrocitoma de grado IIspa
dc.subjectTécnica computacionalspa
dc.subjectSegmentaciónspa
dc.titleLow grade glioma segmentation using an automatic computational technique in magnetic resonance imagingeng
dc.title.alternativeSegmentación de glioma de bajo grado usando una técnica computacional automática en imágenes de resonancia magnéticaspa
dc.typearticleeng
dcterms.referencesStelzer K. Epidemiology and prognosis of brain metastases. Surg Neurol Int. 2013;4(Suppl 4):S192-202.eng
dcterms.referencesMcneill K. Epidemiology of Brain Tumors. Neurol Clin. 2016;34(4):981- 998.eng
dcterms.referencesAmerican Brain Tumor Association (ABTA). About Brain Tumors: A Primer for Patients and Caregivers. 9ª Edition. 2015 ABTA.eng
dcterms.referencesWHO (2007). Cavenee W, Louis D, Ohgaki H et al. Eds. WHO Classification of Tumours of the Central Nervous System. WHO Regional Office Europe.eng
dcterms.referencesWu 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.eng
dcterms.referencesBjoern H. Menze et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 2015; 34(10):1993-2024.eng
dcterms.referencesOstrom 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.eng
dcterms.referencesVera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D Thesis, Universidad de los Andes, Mérida-Venezuela, 2014.spa
dcterms.referencesGudbjartsson H. y Patz S.The rician distribution of noisy MRI data, Magn. Reson. Med. 1995;34 (1):910-914.eng
dcterms.referencesMacovski A. Noise in MRI, Magn. Reson. Med. 1996:36 (1) 494-497.eng
dcterms.referencesKaus 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.eng
dcterms.referencesCho 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.eng
dcterms.referencesSezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004; 13(1):146–165.eng
dcterms.referencesSerra J. Image Analysis Using Mathematical Morphology. London, England: Academic Press, 1982.eng
dcterms.referencesGonzález R., Woods R. Digital Image Processing. USA: Prentice Hall, 2001.eng
dcterms.referencesMukhopadhyay S., Chanda B. A multiscale morphological approach to local contrast enhancement. Signal Processing. 2000; 80(4): 685– 696.eng
dcterms.referencesYu 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.eng
dcterms.referencesW. Pratt. Digital Image Processing. USA: John Wiley & Sons Inc, 2007.eng
dcterms.referencesFischer M., Paredes J., Arce G. Weighted median image sharpeners for the world wide web. IEEE Transactions on Image Processing. 2002;11(7):717-27.eng
dcterms.referencesV. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons, 1998.eng
dcterms.referencesE. 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.eng
dcterms.referencesA. Smola. Learning with kernels. Ph.D Thesis, Technische Universitt Berlin,Germany, 1998.eng
dcterms.referencesB. Scholkopf y A. Smola, Learning with Kernels: Support Vector Machines, Regularization,Optimization, and Beyond. Cambridge, MA , USA: The MIT Press, 2002.eng
dcterms.referencesJ. Suykens, T. V. Gestel, y J. D. Brabanter, Least Squares Support Vector Machines.UK: World Scientific Publishing Co., 2002.eng
dcterms.referencesM. 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.eng

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