Large cells cancer volumetry in chest computed tomography pulmonary images

Lung 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 wi...

Full description

Autores:
Huérfano, Y
Vera, M
Gelvez-Almeida, E
Vera, M I
Valbuena, O
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:20.500.12442/5073
Acceso en línea:
https://hdl.handle.net/20.500.12442/5073
Palabra clave:
Lung cancer
Large Cell Lung Carcinoma
LCLC
Rights
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
id USIMONBOL2_0a45282a52a37c16763fde78edc54397
oai_identifier_str oai:bonga.unisimon.edu.co:20.500.12442/5073
network_acronym_str USIMONBOL2
network_name_str Repositorio Digital USB
repository_id_str
dc.title.eng.fl_str_mv Large cells cancer volumetry in chest computed tomography pulmonary images
title Large cells cancer volumetry in chest computed tomography pulmonary images
spellingShingle Large cells cancer volumetry in chest computed tomography pulmonary images
Lung cancer
Large Cell Lung Carcinoma
LCLC
title_short Large cells cancer volumetry in chest computed tomography pulmonary images
title_full Large cells cancer volumetry in chest computed tomography pulmonary images
title_fullStr Large cells cancer volumetry in chest computed tomography pulmonary images
title_full_unstemmed Large cells cancer volumetry in chest computed tomography pulmonary images
title_sort Large cells cancer volumetry in chest computed tomography pulmonary images
dc.creator.fl_str_mv Huérfano, Y
Vera, M
Gelvez-Almeida, E
Vera, M I
Valbuena, O
Salazar-Torres, J
dc.contributor.author.none.fl_str_mv Huérfano, Y
Vera, M
Gelvez-Almeida, E
Vera, M I
Valbuena, O
Salazar-Torres, J
dc.subject.eng.fl_str_mv Lung cancer
Large Cell Lung Carcinoma
LCLC
topic Lung cancer
Large Cell Lung Carcinoma
LCLC
description Lung 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.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-03-26T23:26:17Z
dc.date.available.none.fl_str_mv 2020-03-26T23:26:17Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.driver.eng.fl_str_mv article
dc.identifier.issn.none.fl_str_mv 17426596
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/5073
identifier_str_mv 17426596
url https://hdl.handle.net/20.500.12442/5073
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.eng.fl_str_mv pdf
dc.publisher.eng.fl_str_mv IOP Publishing
dc.source.eng.fl_str_mv Journal of Physics: Conference Series
Vol. 1414 (2019)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1414/1/012018
bitstream.url.fl_str_mv https://bonga.unisimon.edu.co/bitstreams/a679d484-0fb0-4983-a24c-820744d2f522/download
https://bonga.unisimon.edu.co/bitstreams/4bb02ac2-51dc-4568-aea2-711dac9fd777/download
https://bonga.unisimon.edu.co/bitstreams/5dbc365c-95f9-4505-9987-a846f73195d1/download
https://bonga.unisimon.edu.co/bitstreams/713b34af-6963-48c2-93e0-c657cea30ed5/download
https://bonga.unisimon.edu.co/bitstreams/1e6257e9-ac74-4842-8472-48434083eda7/download
https://bonga.unisimon.edu.co/bitstreams/5360fa81-1ff6-48d6-89ea-40331014e003/download
https://bonga.unisimon.edu.co/bitstreams/0bd2964e-4074-4dda-a561-2904e9fbb70c/download
bitstream.checksum.fl_str_mv 00dd466f462c472869729db689044aa2
4460e5956bc1d1639be9ae6146a50347
733bec43a0bf5ade4d97db708e29b185
f9428af4f699dff625c932dc90ecb808
e3f1ad08ff05600e65636b268fdbd12e
ba8529aadbb64a1993fe8a52e8583920
d3b019e858b9fc75c08555fb5ac28d87
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Digital Universidad Simón Bolívar
repository.mail.fl_str_mv repositorio.digital@unisimon.edu.co
_version_ 1812100534188900352
spelling Huérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fVera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eGelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deVera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363eValbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e9Salazar-Torres, J40a2a6c9-3e39-4994-9b5a-1c6112bd80002020-03-26T23:26:17Z2020-03-26T23:26:17Z201917426596https://hdl.handle.net/20.500.12442/5073Lung 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.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 1414 (2019)https://iopscience.iop.org/article/10.1088/1742-6596/1414/1/012018Lung cancerLarge Cell Lung CarcinomaLCLCLarge cells cancer volumetry in chest computed tomography pulmonary imagesarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Latarjet M and Ruiz A 2004 Anatomía humana (Barcelona: Médica Panamericana)Webb W and Higgins C 2005 Thoracic imaging: pulmonary and cardiovascular radiology (Philadelphia: Lippincott Williams and Wilkins)Barrett J and Keat N 2004 Artifacts in CT: Recognition and avoidance Radiographics 24 1679Wang G and Vannier M 1994 Stair–step artifacts in three-dimensional helical ct: An experimental study Radiology 191 79Kubota T, Jerebko A, Dewan M, Salganicoff M and Krishnan A 2011 Multi Modality State of the Art Medical Image Segmentation and Registration Methodologies ed A El-Baz, U R Acharya, M Mirmehdi and J Suri (Boston: Springer) Density and attachment diagnostic CT pulmonary nodule segmentation with competition-diffusion and new morphological operators 143 Chapter 6Yang B, Xiang D, Yu F and Chen X 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) Medical Imaging (Houston: SPIE) Lung tumor segmentation based on the multi-scale template matching and region growing 10578 HoustonAit B, El Hassani A and Majda A 2018 Lung ct image segmentation using deep neural networks Procedia Computer Science 127 109Petrou M and Bosdogianni P 2003 Image processing the fundamentals (New York: John Wiley & Sons Inc)Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)Burden R and Faires D 2010 Numerical analysis (Mexico: Cengage Learning)Huérfano Y, Vera M, Mar A and Bravo A 2019 Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation Journal of Physics: Conference Series 1160 012003Dice L 1945 Measures of the amount of ecologic association between species Ecology 26 29ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf688352https://bonga.unisimon.edu.co/bitstreams/a679d484-0fb0-4983-a24c-820744d2f522/download00dd466f462c472869729db689044aa2MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/4bb02ac2-51dc-4568-aea2-711dac9fd777/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/5dbc365c-95f9-4505-9987-a846f73195d1/download733bec43a0bf5ade4d97db708e29b185MD53TEXTLCCV_Chest_CTP_images.pdf.txtLCCV_Chest_CTP_images.pdf.txtExtracted texttext/plain16384https://bonga.unisimon.edu.co/bitstreams/713b34af-6963-48c2-93e0-c657cea30ed5/downloadf9428af4f699dff625c932dc90ecb808MD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain16923https://bonga.unisimon.edu.co/bitstreams/1e6257e9-ac74-4842-8472-48434083eda7/downloade3f1ad08ff05600e65636b268fdbd12eMD56THUMBNAILLCCV_Chest_CTP_images.pdf.jpgLCCV_Chest_CTP_images.pdf.jpgGenerated Thumbnailimage/jpeg1284https://bonga.unisimon.edu.co/bitstreams/5360fa81-1ff6-48d6-89ea-40331014e003/downloadba8529aadbb64a1993fe8a52e8583920MD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg3329https://bonga.unisimon.edu.co/bitstreams/0bd2964e-4074-4dda-a561-2904e9fbb70c/downloadd3b019e858b9fc75c08555fb5ac28d87MD5720.500.12442/5073oai:bonga.unisimon.edu.co:20.500.12442/50732024-08-14 21:54:53.256http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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