Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging
Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. T...
- Autores:
-
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Contreras, Yudith
Cuberos, María
Vivas, Marisela
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Hernández, Carlos
Barrera, Doris
Molina, Ángel Valentín
Martínez, Luis Javier
Salazar, Juan
Gelvez, Elkin
Sáenz, Frank
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2525
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2525
- Palabra clave:
- Magnetic resonance brain imaging
Cerebral tumor
Low grade glioma
Grade II astrocytoma
Computational technique
Segmentation
Imágenes cerebrales por resonancia magnética
Tumor cerebral
Gliomas de bajo grado
Astrocitoma de grado II
Técnica computacional
Segmentación
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
Summary: | Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and postprocessing. The percent relative error (PrE) is considered to compare the segmentations of the LGG, generated by a neuro- oncologist manually, with the dilated segmentations of the LGG, obtained automatically. The combination of parameters linked to the lowest PrE, allow establishing the optimal parameters of each computational algorithm that makes up the proposed computational technique. The results allow reporting a PrE of 1.43%, which indicates an excellent correlation between the manual segmentations and those produced by the computational technique developed. |
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