Automatic segmentation of a cerebral glioblastoma using a smart computational technique
We 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-...
- Autores:
-
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Hoyos, Diego
Arias, Yeni
Contreras, Yudith
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Vivas, Marisela
Hernández, Carlos
Barrera, Doris
Molina, Ángel Valentín
Martínez, Luis Javier
Salazar, Juan
Gelvez, Elkin
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2524
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2524
- Palabra clave:
- Brain Tomography
Cerebral Tumor
Glioblastoma
Intelligent Computational Technique
Segmentation
Tomografía cerebral
Tumor cerebral
Glioblastoma
Técnica computacional inteligente
Segmentación
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
Summary: | We 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. |
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