Automatic segmentation of a cerebral glioblastoma using a smart computational technique
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Brain Tomography, Cerebral Tumor, Glioblastoma, Intelligent Computational Technique, Segmentation.Resumen
We propose an intelligent computational technique for theimage segmentation of a type IV brain tumor, identified asmultiform glioblastoma (MGB), which is present in multi-layercomputed tomography images. This technique consists of3 stages developed in the three-dimensional domain. Theyare: pre-processing, segmentation and validation. During thevalidation stage, the Dice coefficient (Dc) is considered inorder to compare the segmentations of the MGB, obtainedautomatically, with the segmentations of the MGB generatedmanually, by a neuro-oncologist. The combination of parameterslinked to the highest Dc, allows to establish the optimalparameters of each of the computational algorithms thatmake up the proposed nonlinear technique. The obtained resultsallow to report a Dc higher than 0.88, validating a goodcorrelation between the manual segmentations and thoseproduced by the computational technique developed.Descargas
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Vera, M., Huérfano, Y., Valbuena, O., Hoyos, D., Arias, Y., Contreras, J., Salazar, W., Vera, M. I., Borrero, M., Vivas, M., Hernández, C., Barrera, D., Molina, Ángel V., Martínez, L. J., Salazar, J., Gelves, E., & Sáenz, F. (2018). Automatic segmentation of a cerebral glioblastoma using a smart computational technique. AVFT – Archivos Venezolanos De Farmacología Y Terapéutica, 37(4). Recuperado a partir de http://saber.ucv.ve/ojs/index.php/rev_aavft/article/view/15676
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