Pulmonary adenocarcinoma characterization using computed tomography images

dc.contributor.authorHuérfano, Y
dc.contributor.authorVera, M
dc.contributor.authorValbuena, O
dc.contributor.authorGelvez-Almeida, E
dc.contributor.authorSalazar-Torres, J
dc.date.accessioned2020-04-14T22:30:28Z
dc.date.available2020-04-14T22:30:28Z
dc.date.issued2019
dc.description.abstractLung 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.eng
dc.format.mimetypepdfeng
dc.identifier.issn17426596
dc.identifier.urihttps://hdl.handle.net/20.500.12442/5104
dc.language.isoengeng
dc.publisherIOP Publishingeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceJournal of Physics: Conference Serieseng
dc.sourceVol. 1408 (2019)eng
dc.source.urihttps://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012004eng
dc.titlePulmonary adenocarcinoma characterization using computed tomography imageseng
dc.typearticleeng
dc.type.driverarticleeng
dcterms.referencesGuyton J 2006 Textbook of medical physiology (USA: Elsevier Saunders)eng
dcterms.referencesAlberg A, Samet J 2003 Epidemiology of lung cancer Chest 123(1) 21seng
dcterms.referencesAit B, El Hassani A, Majda A 2018 Lung ct image segmentation using deep neural networks Procedia Computer Science 127 109eng
dcterms.referencesMingjie X, Shouliang Q, Yong Y, Yueyang T, Lisheng X, Yudong Y, Wei Q 2019 Segmentation of lung parenchyma in ct images using cnn trained with the clustering algorithm generated dataset Biomed Eng Online 18(2) 1eng
dcterms.referencesCharbonnier J, Chung K, Scholten E, Van Rikxoort E, Jacobs C, Sverzellati N, Silva M, Pastorino U, Van Ginneken B, Ciompi F 2018 Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules Sci Rep. 8 646eng
dcterms.referencesKubota T, Jerebko A, Dewan M, Salganicoff M, Krishnan A 2011 Density and attachment agnostic ct pulmonary nodule segmentation with competition-diffusion and new morphological operators Multi modality state-of-the-art medical image segmentation and registration methodologies ed A. El-Baz (Boston: Springer)eng
dcterms.referencesAlilou, M, Beig N, Orooji M, Rajiah P, Velcheti V, Rakshit S, Reddy N, Yang M, Jacono F, Gilkeson R, Linden P, Madabhushi A 2017 An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung ct Med Phys. 44(7) 3556eng
dcterms.referencesWang S, Chen A, Yang L, Cai L, Xie Y, Fujimoto J, Gazdar A, Xiao G 2018 Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome Scientific Reports 8(1) 10393eng
dcterms.referencesYang B, Xiang D, Yu F, Chen X 2018 Lung tumor segmentation based on the multi-scale template matching and region growing Proc. SPIE, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging 10578 105782Qeng
dcterms.referencesHuérfano Y, Vera M, Mar A, Bravo A 2019 Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation. J. Phys. Conf. Ser. 1160 012003eng
dcterms.referencesPratt W 2007 Digital image processing (Unite State of America: John Wiley & Sons Inc)eng
dcterms.referencesMeijering H 2000 Image enhancement in digital x ray angiography doctoral dissertation (Netherlands: Utrecht University)eng
dcterms.referencesGonzález R, Woods R 2001 Digital image processing (New Jersey: Prentice Hall)eng
dcterms.referencesSaénz F, Vera M, Huérfano Y, Molina V, Martinez L, Vera MI, Salazar W, Gelvez E, Salazar J, Valbuena O, Robles H, Bautista M, Arango J 2018 Brain hematoma computational segmentation. J. Phys. Conf. Ser. 1126 012071eng
dcterms.referencesDice L 1945 Measures of the amount of ecologic associationn between species Ecology 26(3) 29eng
oaire.versioninfo:eu-repo/semantics/publishedVersioneng

Archivos

Bloque original
Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Pulmonary_adenocarcinoma_characterizationbyCTI.pdf
Tamaño:
935.01 KB
Formato:
Adobe Portable Document Format
Descripción:
PDF
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
381 B
Formato:
Item-specific license agreed upon to submission
Descripción:

Colecciones