Logotipo del repositorio
Logotipo del repositorio
 

A computational strategy for the identification of pulmonary squamous cell carcinoma in computerized tomography images

Vista sencilla de ítem
dc.contributor.authorHuerfano, Y.
dc.contributor.authorVera, M.
dc.contributor.authorGelvez, E.
dc.contributor.authorSalazar, J.
dc.contributor.authorDel Mar, A.
dc.contributor.authorValbuena, O.
dc.contributor.authorMolina, V.
dc.date.accessioned2019-03-06T20:17:04Z
dc.date.available2019-03-06T20:17:04Z
dc.date.issued2019
dc.description.abstractThe objective of the work is to propose a computational strategy to identify lung squamous cell carcinoma in three-dimensional databases (3D) of multislice computerized tomography. This strategy consists of the pre-processing, segmentation, and post-processing stages. During pre-processing, an anisotropic, gradient-based diffusion algorithm and a filter bank are used to address artifact and image noise issues. During segmentation, the technique called region growing is applied to pre-processed images. Finally, in the post-processing, a morphological dilation filter is used to process the segmented images. In order to make value judgments about the performance of the proposed strategy, the relative percentage error is used to compare the dilated segmentations of the squamous cell carcinoma with the segmentations of the squamous cell carcinoma generated, manually, by a pulmonologist. The combination of parameters linked to the highest PrE, allows establishing the optimal parameters of each of the algorithms that make up the proposed strategy.eng
dc.identifier.issn09767673
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2737eng
dc.language.isoengeng
dc.publisherIOP Publishingeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.sourceJournal of Physicseng
dc.sourceIOP Conf. Series: Journal of Physics: Conf. Series1160 (2019) 012004eng
dc.source.uridoi:10.1088/1742-6596/1160/1/012004eng
dc.subjectCarcinomaeng
dc.subjectTomographyeng
dc.subjectLungs-Diseaseseng
dc.titleA computational strategy for the identification of pulmonary squamous cell carcinoma in computerized tomography imageseng
dc.typeConferenceeng
dcterms.referencesWorld Health Organization (WHO) 2018 Global health estimates 2016: Deaths by cause, age, sex, by country and by region, 2000-2016 (Geneva: World Health Organization)eng
dcterms.referencesWebb W and Higgins C 2005 Lung cancer and bronchopulmonary neoplasms Thoracic imaging: pulmonary and cardiovascular radiology First Edition (USA: Lippincott Williams and Wilkins) Chapter 3 pp 69-116eng
dcterms.referencesNaidich D, Webb R, Muller N, Vlahos I, Krinsky G and Srichai M 2007 Lung cancer computed tomography and magnetic resonance of the thorax Fourth Edition (USA: Lippincott Williams and Wilkins)eng
dcterms.referencesBurlutskiy N, Backman M, Gu F, Kajland L and Micke P 2018 Deep Learning Framework for Automatic Diagnosis in Lung Cancer 1st Conference on Medical Imaging with Deep Learning (MIDL 2018) (Amsterdam: OpenReview)eng
dcterms.referencesFaletra F, Pandian N and Ho S 2008 Anatomy of the heart by multislice computed tomography (UK: Wiley)eng
dcterms.referencesYang B, Xiang D, Yu F and 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.referencesAit B, El Hassani A and Majda A 2018 Lung CT Image Segmentation using deep neural networks Computer Science 127 109eng
dcterms.referencesVera M et al. 2018 Automatic segmentation of subdural hematomas using a computational technique based on smart operators Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) (Porto: IEEE)eng
dcterms.referencesPratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)eng
dcterms.referencesNadal E, Rupérez Moreno MJ, Martinez-Sanchis S, Monserrat Aranda C, Tur Valiente M and Fuenmayor Fernández FJ 2017 Evaluación basada en el método del gradiente de las propiedades elásticas de tejidos humanos in vivo Revista UIS Ingenierías 16(1) 15eng
dcterms.referencesPerona P and Malik J 1990 Scalespace and edge detection using anisotropic diffusion IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7) 629eng

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
A computational strategy for the identification of pulmonary squamous.pdf
Tamaño:
690.28 KB
Formato:
Adobe Portable Document Format
Descripción:
PDF
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
368 B
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Artículos

Universidad Simón Bolívar: Todos los derechos reservados / Sistema de biblioteca