A computational methodology for the staging of lung tumors considering geometric descriptors
Lung diseases diagnosis, specifically the presence of lung tumors, is usually performed with the support of radiological techniques. Computed tomography is the most widely used imaging technique to confirm the presence of this disease. When several researchers require identifying the morphology of t...
- Autores:
-
Vera, Miguel
Huérfano, Yoleidy
Bravo, Antonio
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/6837
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/6837
http://www.revhipertension.com/rlh_3_2020/16_a_omputational_methodology.pdf
- Palabra clave:
- Computerized tomography
Lung tumors
Segmentation
Geometric descriptors
Tomografía computarizada
Tumores pulmonares
Segmentación
Descriptores geométricos
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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A computational methodology for the staging of lung tumors considering geometric descriptors |
| dc.title.translated.spa.fl_str_mv |
Una metodología para la estadificación de tumores pulmonares considerandos descriptores geométricos |
| title |
A computational methodology for the staging of lung tumors considering geometric descriptors |
| spellingShingle |
A computational methodology for the staging of lung tumors considering geometric descriptors Computerized tomography Lung tumors Segmentation Geometric descriptors Tomografía computarizada Tumores pulmonares Segmentación Descriptores geométricos |
| title_short |
A computational methodology for the staging of lung tumors considering geometric descriptors |
| title_full |
A computational methodology for the staging of lung tumors considering geometric descriptors |
| title_fullStr |
A computational methodology for the staging of lung tumors considering geometric descriptors |
| title_full_unstemmed |
A computational methodology for the staging of lung tumors considering geometric descriptors |
| title_sort |
A computational methodology for the staging of lung tumors considering geometric descriptors |
| dc.creator.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Bravo, Antonio |
| dc.contributor.author.none.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Bravo, Antonio |
| dc.subject.eng.fl_str_mv |
Computerized tomography Lung tumors Segmentation Geometric descriptors |
| topic |
Computerized tomography Lung tumors Segmentation Geometric descriptors Tomografía computarizada Tumores pulmonares Segmentación Descriptores geométricos |
| dc.subject.spa.fl_str_mv |
Tomografía computarizada Tumores pulmonares Segmentación Descriptores geométricos |
| description |
Lung diseases diagnosis, specifically the presence of lung tumors, is usually performed with the support of radiological techniques. Computed tomography is the most widely used imaging technique to confirm the presence of this disease. When several researchers require identifying the morphology of these tumors, they deal problems related to the poor delimitation of the borders associated with the anatomical structures that compound the lung, Poisson noise, the streak artifact and the non-homogeneity of gray levels that define each object in the chest images. In this paper, a methodology has been presented to identify in which stage (staging) the mentioned tumors are. For this, first, anisotropic diffusion filter and magnitude of the gradient filter are used in order to address the aforementioned problems. Second, a smart operator and the level set lgorithm are used to segment lung tumors. Finally, considering these segmentations, a set of geometric descriptors is obtained, and it allows staging of such tumors to be precisely established, generating results that are in high correspondence with the reference data, linked to the analyzed tagged images. |
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2020 |
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2020-12-03T15:34:40Z |
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2020-12-03T15:34:40Z |
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2020 |
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Artículo científico |
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18564550 |
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https://hdl.handle.net/20.500.12442/6837 |
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http://www.revhipertension.com/rlh_3_2020/16_a_omputational_methodology.pdf |
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18564550 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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pdf |
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Sociedad Venezolana de Farmacología Clínica y Terapéutica Sociedad Latinoamericana de Hipertensión |
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Revista Latinoamericana de Hipertensión Vol. 15, No. 3 (2020) |
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Universidad Simón Bolívar |
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Vera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e3Huérfano, Yoleidy769899ba-e6a1-4144-95c2-ff4614f93578Bravo, Antonio07aba3dd-3344-4237-9ad4-3d40d655e9152020-12-03T15:34:40Z2020-12-03T15:34:40Z202018564550https://hdl.handle.net/20.500.12442/6837http://www.revhipertension.com/rlh_3_2020/16_a_omputational_methodology.pdfLung diseases diagnosis, specifically the presence of lung tumors, is usually performed with the support of radiological techniques. Computed tomography is the most widely used imaging technique to confirm the presence of this disease. When several researchers require identifying the morphology of these tumors, they deal problems related to the poor delimitation of the borders associated with the anatomical structures that compound the lung, Poisson noise, the streak artifact and the non-homogeneity of gray levels that define each object in the chest images. In this paper, a methodology has been presented to identify in which stage (staging) the mentioned tumors are. For this, first, anisotropic diffusion filter and magnitude of the gradient filter are used in order to address the aforementioned problems. Second, a smart operator and the level set lgorithm are used to segment lung tumors. Finally, considering these segmentations, a set of geometric descriptors is obtained, and it allows staging of such tumors to be precisely established, generating results that are in high correspondence with the reference data, linked to the analyzed tagged images.El diagnóstico de enfermedades del pulmón, específicamente la presencia de tumores pulmonares, suele efectuarse con el apoyo de técnicas radiológicas. La tomografía computarizada es la técnica imagenológica más utilizada para confirmar la presencia de esta enfermedad. Cuando diversos investigadores desean generar la morfología de esos tumores se enfrentan a problemas relativos a la pobre delimitación de las fronteras asociadas con las estructuras anatómicas que conforman el pulmón, el ruido poisoniano, el artefacto tipo escalera y la no-homogeneidad de los niveles de gris que definen cada objeto en las imágenes de tórax. En el presente artículo, se presenta una metodología para identificar en cual estadio (estadificación) se encuentran los mencionados tumores. Para ello, en primer lugar, los filtros de difusión anisotrópica con curvatura y magnitud del gradiente son utilizados a fin de abordar los mencionados problemas. En segundo lugar, un operador inteligente y el algoritmo denominado conjuntos de nivel son utilizados para segmentar los tumores pulmonares. Finalmente, considerando estas segmentaciones se obtiene un conjunto de descriptores geométricos que permite establecer con precisión la estadificación de tales tumores, generando resultados que están en alta correspondencia con los datos de referencia, vinculados con las imágenes etiquetadas analizadas.pdfengSociedad Venezolana de Farmacología Clínica y TerapéuticaSociedad Latinoamericana de HipertensiónAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Revista Latinoamericana de HipertensiónVol. 15, No. 3 (2020)Computerized tomographyLung tumorsSegmentationGeometric descriptorsTomografía computarizadaTumores pulmonaresSegmentaciónDescriptores geométricosA computational methodology for the staging of lung tumors considering geometric descriptorsUna metodología para la estadificación de tumores pulmonares considerandos descriptores geométricosinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Anita C, Sonit SS. Lung cancer detection on CT images using image processing. IEEE Comput Society, 4, 142-46, 2012Zanella R, Boccacci P, Zanni L, Bertero M. Inverse Problems in Computational Methods for Inverse Problems in Imaging, 25(4) 1, 2009Barrett J, Keat N Artifacts in CT: Recognition and avoidance1 Radiographics 24(6) 1679, 2004.Porta R. The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours. Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer. (2) 706, 2007.Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE The IASLC Lung Cancer Staging Project: Proposals for revisión of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol (11) 39, 2016Álvarez, L., Guichard, F., Lions, P.L., Morel, J.M. (1993). Axioms and Fundamental Equations of Image Processing. Arch. Ration. Mech. Anal. 123, 199-257.Witkin, A. (1983). Scale-space filtering. Int’l Joint Conf. Artificial Intelligence, 1019- 1021.Koenderink J. The structure of images. Biological Cybernetics, 50, 363-370, 1984.Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell. 12, 629-639, 1990.Vera M, Gonzalez E, Húerfano Y, Gelvez E, Valbuena O. New anisotropic diffusion operator in images filtering. Journal of Physics: Conf. Series 1448-012019 doi:10.1088/1742-6596/1448/1/012019, 2020.Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc).Sapiro G. Geometric Partial Differential Equations and Image Analysis. Cambridge, UK,: Cambridge University Press, 2001.G. Dharanibai y J. P. Raina, Gaussian mixture model based level set technique for automated segmentation of cardiac MR images. International Journal of Environmental Science and Technology, vol. 3, no. 4, pp. 2970–2976, 2011.Lavanya M, Muthu P. Lung lesion detection in ct scan images using the fuzzy local information cluster means (FLICM) automatic segmentation algorithm and back propagation network classification. Asian Pacific Journal of Cancer Prevention 18 3395, 2017.Dice, L. Measures of the amount of ecologic association between species. Ecology. 26: 297-302, 1945Zhu H, Rohwer R. No free lunch for cross-validation. Neural Computation, 8(7):1421-1426, 1996.Vera M 2014 Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi–corte. Tesis doctoral. Universidad de Los Andes-Mérida-VenezuelaHoyos N, Montoro F, García J, Morales B, Pavón M Cáncer de pulmón: ¿qué hay de nuevo? Rev Patol Respir. 20(2) 47, 2017.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf788006https://bonga.unisimon.edu.co/bitstreams/3975a307-973f-40aa-839a-48a8d83145ab/download57b8e512da24f55798c6160a25f2b7c1MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/964725b7-0e3c-46a2-9082-2eb0c07edcfb/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/bad3e3c5-231c-4dfa-b731-67ba5b3fa806/download733bec43a0bf5ade4d97db708e29b185MD53TEXTComputational_methodology_Lung_tumors.pdf.txtComputational_methodology_Lung_tumors.pdf.txtExtracted texttext/plain18947https://bonga.unisimon.edu.co/bitstreams/3cc0b1df-2cd7-4211-8bb6-cca7600a173c/downloadc3dfc9e7766902264de4cd790d295d93MD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain19208https://bonga.unisimon.edu.co/bitstreams/c89b0c3a-030f-4697-a613-c6415dbaceb6/download201e5d0df6a447fe833c4ec6a8241a0bMD56THUMBNAILComputational_methodology_Lung_tumors.pdf.jpgComputational_methodology_Lung_tumors.pdf.jpgGenerated Thumbnailimage/jpeg1894https://bonga.unisimon.edu.co/bitstreams/f2f1e138-4f68-4d12-9b6a-55fd61306bbf/downloadbadec43e3543f7a074c4d3f8dc4918abMD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg6025https://bonga.unisimon.edu.co/bitstreams/a614efe8-6230-4393-abbf-622796deff19/download557389d084498a1d7b7376e9b0fea9beMD5720.500.12442/6837oai:bonga.unisimon.edu.co:20.500.12442/68372024-08-14 21:53:08.799http://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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 |