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Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging

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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.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.identifier.issn26107988
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2521
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_2018/6_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
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