Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging

Through this work we propose a computational technique for the segmentation of a brain tumor, identified as meningioma (MGT), which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain: pre-processing, segmentation and post-processing. The percent relative error (PrE) is considered to compare the segmentations of the MGT, generated by a neuro-oncologist manually, with the dilated segmentations of the MGT, obtained automatically. The combination of parameters linked to the lowest PrE, provides the optimal parameters of each computational algorithm that makes up the proposed computational technique. Results allow reporting a PrE of 1.44%, showing an excellent correlation between the manual segmentations and those produced by the computational technique developed.

• Autores: Vera, Miguel; Huérfano, Yoleidy; Molina, Ángel Valentín; Valbuena, Oscar; Vivas, Marisela; Cuberos, María; Salazar, Williams; Vera, María Isabel; Borrero, Maryury; Hernández, Carlos; Barrera, Doris; Martínez, Luis Javier; Salazar, Juan; Gelvez, Elkin; Contreras, Yudith; Sáenz, Frank
• Fecha de publicación: 2018
• Institución: Universidad Simón Bolívar
• Repositorio: Repositorio Digital USB
• Idioma: eng
• OAI Identifier: oai:bonga.unisimon.edu.co:20.500.12442/2521
• Acceso en línea: http://hdl.handle.net/20.500.12442/2521
• Palabra clave: Magnetic resonance brain imaging; Brain tumor; Meningioma; Computational technique; Segmentation; Imágenes cerebrales por resonancia magnética; Tumor cerebral; Meningioma; Técnica computacional; Segmentación
• License: Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional