Pulmonary adenocarcinoma characterization using computed tomography images
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Fecha
2019
Autores
Huérfano, Y
Vera, M
Valbuena, O
Gelvez-Almeida, E
Salazar-Torres, J
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Editor
IOP Publishing
Resumen
Lung cancer is one of the pathologies that sensitively affects the health of human
beings. Particularly, the pathology called pulmonary adenocarcinoma represents 25% of all lung
cancers. In this research, we propose a semiautomatic technique for the characterization of a
tumor (adenocarcinoma type), present in a three-dimensional pulmonary computed tomography
dataset. Following the basic scheme of digital image processing, first, a bank of smoothing filters
and edge detectors is applied allowing the adequate preprocessing over the dataset images. Then,
clustering methods are used for obtaining the tumor morphology. The relative percentage error
and the accuracy rate were the metrics considered to determine the performance of the proposed
technique. The values obtained from the metrics used reflect an excellent correlation between
the morphology of the tumor, generated manually by a pneumologist and the values obtained by
the proposed technique. In the clinical and surgical contexts, the characterization of the detected
lung tumor is made in terms of volume occupied by the tumor and it allows the monitoring of
this disease as well as the activation of the respective protocols for its approach.