Automatic segmentation of a cerebral glioblastoma using a smart computational technique

Miguel Vera, Yoleidy Huérfano, Oscar Valbuena, Diego Hoyos, Yeni Arias, Judith Contreras, William Salazar, María Isabel Vera, Maryury Borrero, Marisela Vivas, Carlos Hernández, Doris Barrera, Ángel Valentín Molina, Luis Javier Martínez, Juan Salazar, Elkin Gelves, Frank Sáenz


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.


Texto completo: PDF


  • No hay Refbacks actualmente.

world map hits counter
web counter
web counter visitors by country counter
flag counter