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Automatic segmentation of a cerebral glioblastoma using a smart computational technique

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dc.contributor.authorVera, Miguel
dc.contributor.authorHuérfano, Yoleidy
dc.contributor.authorValbuena, Oscar
dc.contributor.authorHoyos, Diego
dc.contributor.authorArias, Yeni
dc.contributor.authorContreras, Yudith
dc.contributor.authorSalazar, Williams
dc.contributor.authorVera, María Isabel
dc.contributor.authorBorrero, Maryury
dc.contributor.authorVivas, Marisela
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.date.accessioned2019-01-25T14:52:01Z
dc.date.available2019-01-25T14:52:01Z
dc.date.issued2018
dc.description.abstractWe propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and validation. During the validation stage, the Dice coefficient (Dc) is considered in order to compare the segmentations of the MGB, obtained automatically, with the segmentations of the MGB generated manually, by a neuro-oncologist. The combination of parameters linked to the highest Dc, allows to establish the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow to report a Dc higher than 0.88, validating a good correlation between the manual segmentations and those produced by the computational technique developed.eng
dc.description.abstractProponemos una técnica computacional inteligente para la segmentación de imágenes de un tumor cerebral tipo IV, identificado como glioblastoma multiforme (MGB), que está presente en imágenes de tomografía computarizada de múltiples capas. Esta técnica consiste en 3 etapas desarrolladas en el dominio tridimensional. Ellos son: preprocesamiento, segmentación y validación. Durante la etapa de validación, se considera el coeficiente de dados (Dc) para comparar las segmentaciones del MGB, obtenidas automáticamente, con las segmentaciones del MGB generado manualmente, por un neurooncólogo. La combinación de parámetros vinculados a la mayor Dc permite establecer los parámetros óptimos de cada uno de los algoritmos computacionales que conforman la técnica no lineal propuesta. Los resultados obtenidos permiten informar una Dc superior a 0,88, validando una buena correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada.spa
dc.identifier.issn26107988
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2524
dc.language.isoengeng
dc.publisherSociedad Venezolana de Farmacología Clínica y Terapéuticaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
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_2018/5_automatic_segmentation_of_a_cerebral.pdfeng
dc.subjectBrain Tomographyeng
dc.subjectCerebral Tumoreng
dc.subjectGlioblastomaeng
dc.subjectIntelligent Computational Techniqueeng
dc.subjectSegmentationeng
dc.subjectTomografía cerebralspa
dc.subjectTumor cerebralspa
dc.subjectGlioblastomaspa
dc.subjectTécnica computacional inteligentespa
dc.subjectSegmentaciónspa
dc.titleAutomatic segmentation of a cerebral glioblastoma using a smart computational techniqueeng
dc.title.alternativeSegmentación automática de glioblastoma cerebral usando una técnica computacional inteligentespa
dc.typearticleeng
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