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dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.contributor.authorVera, Miguel
dc.contributor.authorHuérfano, Yoleidy
dc.contributor.authorMolina, Ángel Valentín
dc.contributor.authorValbuena, Oscar
dc.contributor.authorVivas, Marisela
dc.contributor.authorCuberos, María
dc.contributor.authorSalazar, Williams
dc.contributor.authorVera, María Isabel
dc.contributor.authorBorrero, Maryury
dc.contributor.authorHernández, Carlos
dc.contributor.authorBarrera, Doris
dc.contributor.authorMartínez, Luis Javier
dc.contributor.authorSalazar, Juan
dc.contributor.authorGelvez, Elkin
dc.contributor.authorContreras, Yudith
dc.contributor.authorSáenz, Frank
dc.date.accessioned2019-01-24T22:46:58Z
dc.date.available2019-01-24T22:46:58Z
dc.date.issued2018
dc.identifier.issn26107988
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2521
dc.description.abstractThrough 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.eng
dc.description.abstractEste 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.spa
dc.language.isoengeng
dc.publisherSociedad Venezolana de Farmacología Clínica y Terapéuticaspa
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_2018/6_automatic_segmentation_of_a_meningioma.pdfeng
dc.subjectMagnetic resonance brain imagingeng
dc.subjectBrain tumoreng
dc.subjectMeningiomaeng
dc.subjectComputational techniqueeng
dc.subjectSegmentationspa
dc.subjectImágenes cerebrales por resonancia magnéticaspa
dc.subjectTumor cerebralspa
dc.subjectMeningiomaspa
dc.subjectTécnica computacionalspa
dc.subjectSegmentaciónspa
dc.titleAutomatic segmentation of a meningioma using a computational technique in magnetic resonance imagingeng
dc.title.alternativeSegmentación automática de un meningioma usando una técnica computacional 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 KA. 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.referencesBurger, Scheithauer, and Vogel, Surgical Pathology of the Nervous System and Its Coverings. 4th edition. Churchill Livingstone, Nueva York, 2002.eng
dcterms.referencesVera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D. dissertation, 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.referencesSanjuán 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):241-257eng
dcterms.referencesHsieh 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.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.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, pp. 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, pp. 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, pp. 193–200.eng
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