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dc.contributor.authorHuérfano, Y
dc.contributor.authorVera, M
dc.contributor.authorGelvez-Almeida, E
dc.contributor.authorVera, M I
dc.contributor.authorValbuena, O
dc.contributor.authorSalazar-Torres, J
dc.description.abstractLung cancer is the leading oncological cause of death in the world. As for carcinomas, they represent between 90% and 95% of lung cancers; among them, non-small cell lung cancer is the most common type and the large cell carcinoma, the pathology on which this research focuses, is usually detected with the computed tomography images of the thorax. These images have three big problems: noise, artifacts and low contrast. The volume of the large cell carcinoma is obtained from the segmentations of the cancerous tumor generated, in a semi-automatic way, by a computational strategy based on a combination of algorithms that, in order to address the aforementioned problems, considers median and gradient magnitude filters and an unsupervised grouping technique for generating the large cell carcinoma morphology. The results of high correlation between the semi-automatic segmentations and the manual ones, drawn up by a pulmonologist, allow us to infer the excellent performance of the proposed technique. This technique can be useful in the detection and monitoring of large cell carcinoma and if it is considering this kind of computational strategy, medical specialists can establish the clinic or surgical actions oriented to address this pulmonary pathology.eng
dc.publisherIOP Publishingeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.sourceJournal of Physics: Conference Serieseng
dc.sourceVol. 1414 (2019)eng
dc.subjectLung cancereng
dc.subjectLarge Cell Lung Carcinomaeng
dc.titleLarge cells cancer volumetry in chest computed tomography pulmonary imageseng
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