Semi-automatic detection of hepatic tumor in computed tomography images

In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of th...

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Autores:
Sáenz, F
Vera, M
López, J
Huérfano, Y
Valbuena, O
Vera, M I
Gelvez-Almeida, E
Salazar-Torres, J
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/5098
Acceso en línea:
https://hdl.handle.net/20.500.12442/5098
Palabra clave:
Rights
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Description
Summary:In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of the objects present in those images and, on the other hand, generate the liver tumor morphology. For this, the sequence of techniques composed of gaussian smoothing, gradient magnitude, median filter, region growing and binary morphological dilation are used. The value obtained, for the metric called Dice score, show a good correlation between manual segmentation, performed by a hepatologist, and the tumor segmentation obtained using the proposed technique. This type of segmentation is the extreme utility for the characterization of hepatic tumors and the planning of the clinical behavior to be followed in the treatment of this human liver disease.