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...
- 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
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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 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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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 |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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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 |
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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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 |