Uncertainty as key element in the analysis of X–ray angiography images

The X–ray angiography images are routinely used to assess the blood vessels. The acquisition procedure considers a medical imaging system which allows obtaining views of the vessel while the blood flows thought them. The X–ray source is influenced on the region to be viewed and then, the projection...

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Autores:
Bravo, A
Vera, M
Huérfano, Y
Manrique, Y
Valbuena, O
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/6363
Acceso en línea:
https://hdl.handle.net/20.500.12442/6363
https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012021/pdf
Palabra clave:
X–ray angiography images
Medical imaging system
Voronary vessel
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.title.eng.fl_str_mv Uncertainty as key element in the analysis of X–ray angiography images
title Uncertainty as key element in the analysis of X–ray angiography images
spellingShingle Uncertainty as key element in the analysis of X–ray angiography images
X–ray angiography images
Medical imaging system
Voronary vessel
title_short Uncertainty as key element in the analysis of X–ray angiography images
title_full Uncertainty as key element in the analysis of X–ray angiography images
title_fullStr Uncertainty as key element in the analysis of X–ray angiography images
title_full_unstemmed Uncertainty as key element in the analysis of X–ray angiography images
title_sort Uncertainty as key element in the analysis of X–ray angiography images
dc.creator.fl_str_mv Bravo, A
Vera, M
Huérfano, Y
Manrique, Y
Valbuena, O
dc.contributor.author.none.fl_str_mv Bravo, A
Vera, M
Huérfano, Y
Manrique, Y
Valbuena, O
dc.subject.eng.fl_str_mv X–ray angiography images
Medical imaging system
Voronary vessel
topic X–ray angiography images
Medical imaging system
Voronary vessel
description The X–ray angiography images are routinely used to assess the blood vessels. The acquisition procedure considers a medical imaging system which allows obtaining views of the vessel while the blood flows thought them. The X–ray source is influenced on the region to be viewed and then, the projection of the all anatomical structures in the champ of view is shown through an image intensifier. The information of the blood vessel is impacted for the other structures. Additionally, the blood and the contrast product required in the acquisition are not mixed homogeneously, producing artifacts in the images. Finally, the noise is also an impact factor in the quality of the angiography images. In the coronary vessel case, the branches of the network are superposed. In this paper, an enhancement procedure to diminish the uncertainty associated to X–ray angiography images is reported. The relation between two versions of the angiograms is determined using a fuzzy connector considering that this relation diminishes the images intrinsic uncertainty. These versions correspond with images filtered with low-pass and high-pass image filters, respectively. The technique is tested with images of the coronary and kidney vessels. The qualitative results show a good enhanced of the angiography images.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-08-27T02:14:39Z
dc.date.available.none.fl_str_mv 2020-08-27T02:14:39Z
dc.date.issued.none.fl_str_mv 2020
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dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.spa.spa.fl_str_mv Artículo científico
dc.identifier.issn.none.fl_str_mv 17426596
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/6363
dc.identifier.url.none.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012021/pdf
identifier_str_mv 17426596
url https://hdl.handle.net/20.500.12442/6363
https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012021/pdf
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv openAccess
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
dc.source.none.fl_str_mv Vol. 1547 No. 1 (2020)
institution Universidad Simón Bolívar
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spelling Bravo, A3e2b5f23-22e5-41e6-a979-50e352e3d4beVera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eHuérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fManrique, Ya2c18bc7-df03-4e42-95e9-7ee7a9452d79Valbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e92020-08-27T02:14:39Z2020-08-27T02:14:39Z202017426596https://hdl.handle.net/20.500.12442/6363https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012021/pdfThe X–ray angiography images are routinely used to assess the blood vessels. The acquisition procedure considers a medical imaging system which allows obtaining views of the vessel while the blood flows thought them. The X–ray source is influenced on the region to be viewed and then, the projection of the all anatomical structures in the champ of view is shown through an image intensifier. The information of the blood vessel is impacted for the other structures. Additionally, the blood and the contrast product required in the acquisition are not mixed homogeneously, producing artifacts in the images. Finally, the noise is also an impact factor in the quality of the angiography images. In the coronary vessel case, the branches of the network are superposed. In this paper, an enhancement procedure to diminish the uncertainty associated to X–ray angiography images is reported. The relation between two versions of the angiograms is determined using a fuzzy connector considering that this relation diminishes the images intrinsic uncertainty. These versions correspond with images filtered with low-pass and high-pass image filters, respectively. The technique is tested with images of the coronary and kidney vessels. The qualitative results show a good enhanced of the angiography images.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 1547 No. 1 (2020)X–ray angiography imagesMedical imaging systemVoronary vesselUncertainty as key element in the analysis of X–ray angiography imagesinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Waldman S and Campbell R 2011 Imaging of Pain (Philadelphia: W B Saunders)Nimsky C and Carl B 2017 Neurosurgery Clinics of North America 28(4) 453Athanasiou L, Fotiadis D and Michalis L 2017 Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging (Oxford: Academic Press)Preim B and Botha C 2014 Visual Computing for Medicine (Boston: Morgan Kaufmann)Nakabayashi K, Okada H, Sugiura R and Oka T 2016 Journal of Cardiology Cases 13(6) 171Elliott W 2007 Secondary hypertension: Renovascular hypertension Hypertension ed Black H R and Elliott W J (Philadelphia: W B Saunders) chap 8Morris P, Ryan D, Morton A, Lycett R, Lawford P, Hose D and Gunn J 2013 JACC. Cardiovascular Interventions 6(2) 149Castro M, Putman C and Cebral J 2006 Academic Radiology 13(7) 811Bush R, Najibi S, MacDonald M, Lin P, Chaikof E, Martin L and Lumsden A 2001 Journal of Vascular Surgery 33(5) 1041Gianrossi R, Detrano R, Colombo A and Froelicher V 1990 American Heart Journal 120(5) 1179Kotre C and Marshall N 2001 Radiation Protection Dosimetry 94(1-2) 73Crocker E, Tutton R and Bowen J 1986 Journal of Vascular Surgery 4(2) 157Zeng Z, Kang R, Wen M and Zio E 2018 Information Sciences 429 26Zadeh L, Fu K S, Tanaka K and Shimura M 1975 Fuzzy Sets and their Applications to Cognitive and Decision Processes (New York: Academic Press)Cheng H and Xu H 2000 Pattern Recognition 33(5) 809Chacón M, Aguilar L and Delgado A 2003 Definition and applications of a fuzzy image processing scheme Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop (Pine Mountain: IEEE) p 102Chacón M, Aguilar L and Delgado A 2003 Definition and applications of a fuzzy image processing scheme Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop (Pine Mountain: IEEE) p 102Bloch I 2015 Fuzzy Sets Systems 281 280Klir G, Clair U and Yuan B 1997 Fuzzy Set Theory: Foundations and Applications (New York: Prentice Hall)Herrera F, Lozano M and Verdegay J 1998 Artificial Intelligence Review 12(4) 265Mizumoto M 1989 Fuzzy Sets Systems 31(2) 217Mizumoto M 1989 Fuzzy Sets Systems 32(1) 45Russ J and Neal F 2018 The Image Processing Handbook (Boca Raton: CRC Press)Loizou C and Pattichis C 2015 Despeckle Filtering for Ultrasound Imaging and Video: Algorithms and Software vol 1 (Williston: Morgan & Claypool Publishers)Tyagi V 2018 Understanding Digital Image Processing (Boca Raton: CRC Press)Perona P and Malik J 1990 IEEE Transaction on Pattern Analysis and Machine Intelligence 12(7) 629Dhawan A 2011 Medical Image Analysis (New Jersey: Wiley)CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/e03727bf-2b8d-4f48-9a5e-9f0d927e7da3/download4460e5956bc1d1639be9ae6146a50347MD52ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf1237289https://bonga.unisimon.edu.co/bitstreams/5d8f134b-bc31-4db3-94e2-21c4a4457349/download132241ead6134e15b69e9353678cd975MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/8c3711d9-a10f-4e9c-a610-3abe5b98b032/download733bec43a0bf5ade4d97db708e29b185MD53TEXTPDF.pdf.txtPDF.pdf.txtExtracted texttext/plain17237https://bonga.unisimon.edu.co/bitstreams/d9d07d61-ce8f-48fe-84d7-67607cfc9ec5/downloade26b582c0aaef644f9d287d47fddd0a7MD54THUMBNAILPDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg1407https://bonga.unisimon.edu.co/bitstreams/dd40d760-86a4-46f9-ad29-2932fc3575b4/download5b126ca8b727624c1af60be4f9f10173MD5520.500.12442/6363oai:bonga.unisimon.edu.co:20.500.12442/63632024-08-14 21:52:24.962http://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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