Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures

A spatial geometric plane is defined by the three-dimensional coordinates of a pair of spatial points and the direction that the normal vector establishes, which is formed by joining those points by means of an oriented line segment. This type of planes, in three-dimensional images, is extremely use...

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Autores:
Vera, M
Valbuena, O
Huérfano, Y
Vera, M I
Gelvez-Almeida, E
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:20.500.12442/5114
Acceso en línea:
https://hdl.handle.net/20.500.12442/5114
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network_acronym_str USIMONBOL2
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repository_id_str
dc.title.eng.fl_str_mv Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
title Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
spellingShingle Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
title_short Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
title_full Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
title_fullStr Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
title_full_unstemmed Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
title_sort Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
dc.creator.fl_str_mv Vera, M
Valbuena, O
Huérfano, Y
Vera, M I
Gelvez-Almeida, E
Salazar-Torres, J
dc.contributor.author.none.fl_str_mv Vera, M
Valbuena, O
Huérfano, Y
Vera, M I
Gelvez-Almeida, E
Salazar-Torres, J
description A spatial geometric plane is defined by the three-dimensional coordinates of a pair of spatial points and the direction that the normal vector establishes, which is formed by joining those points by means of an oriented line segment. This type of planes, in three-dimensional images, is extremely useful as an alternative solution to the problem of low contrast that exhibit the anatomical structures present in cardiac computed tomography images. To do this, after using a predetermined filter bank and in order to define a region of interest, a smart operator based on least squares support vector machines is trained and validated in order to detect the aforementioned coordinates which enables the location of the plane, in the three-dimensional space that contains the considered images. Once the structure that is required to segment is identified, a discriminant function is used that cancels all information not linked to this structure. In this work, the segmentation of the left ventricle, based on region growing technique, is firstly considered and then the left atrium is segmented considering region growing technique and an inverse discriminant function. The results show an excellent correspondence relationship when the spatial union of both structures is made.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-04-15T20:11:40Z
dc.date.available.none.fl_str_mv 2020-04-15T20:11:40Z
dc.type.eng.fl_str_mv article
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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/20.500.12442/5114
identifier_str_mv 17426596
url https://hdl.handle.net/20.500.12442/5114
dc.language.iso.eng.fl_str_mv eng
language eng
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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. 1408 (2019)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012005
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spelling Vera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eValbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e9Huérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fVera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363eGelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deSalazar-Torres, J40a2a6c9-3e39-4994-9b5a-1c6112bd80002020-04-15T20:11:40Z2020-04-15T20:11:40Z201917426596https://hdl.handle.net/20.500.12442/5114A spatial geometric plane is defined by the three-dimensional coordinates of a pair of spatial points and the direction that the normal vector establishes, which is formed by joining those points by means of an oriented line segment. This type of planes, in three-dimensional images, is extremely useful as an alternative solution to the problem of low contrast that exhibit the anatomical structures present in cardiac computed tomography images. To do this, after using a predetermined filter bank and in order to define a region of interest, a smart operator based on least squares support vector machines is trained and validated in order to detect the aforementioned coordinates which enables the location of the plane, in the three-dimensional space that contains the considered images. Once the structure that is required to segment is identified, a discriminant function is used that cancels all information not linked to this structure. In this work, the segmentation of the left ventricle, based on region growing technique, is firstly considered and then the left atrium is segmented considering region growing technique and an inverse discriminant function. The results show an excellent correspondence relationship when the spatial union of both structures is made.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. 1408 (2019)https://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012005Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structuresarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Vera M 2014 Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multicorte (Mérida: Universidad de Los Andes)Bravo A, Mantilla J, Clemente J, Vera M, Medina R 2010 Left ventricle segmentation and motion analysis in multi-slice computerized tomography Biomedical image analysis and machine learning technologies: applications and techniques ed F Gonzalez (New York: Medical Information Science Reference) p 307Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D 2008 Four–chamber heart modeling and automatic segmentation for 3d cardiac ct volumes using marginal space learning and steerable features IEEE Transactions on Medical Imaging 27(11) 1668Huérfano Y, Vera M, Del Mar A, Bravo A 2019 Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation J. Phys. Conf. Ser. 1160 012003González R, Woods R 2001 Digital image processing (New Jersey: Prentice Hall)Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)Koenderink J 1984 The structure of images Biological Cybernetics 50 363Primak A, McCollough C, Bruesewitz M, Zhang J, Fletcher J 2006 Relationship between noise, dose, and pitch in cardiac multi–detector row ct Radiographics 26(6) 1785Vera M, Medina R, Del Mar A, Arellano J, Huérfano Y, Bravo A 2019 An automatic technique for left ventricle segmentation from msct cardiac volumes J. Phys. Conf. Ser. 1160 012001Bravo A, Vera M, Garreau M, Medina R 2011 Three–dimensional segmentation of ventricular heart chambers from multi–slice computerized tomography: An hybrid approach Proc. Digital Information and Communication Technology and Its Applications (France: Springer) 166 287Petrou M, Bosdogianni P 2003 Image processing the fundamentals (UK: Wiley)Dice L 1945 Measures of the amount of ecologic association between species Ecology 26(3) 29ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf1179421https://bonga.unisimon.edu.co/bitstreams/ded5d811-6c06-45fe-b71b-b822a44118d6/download793d0ba7de0ca296a02687728fab856fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/f57a8f56-7665-4e06-94cf-41533ded08d1/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/39bd72a4-805b-49f6-af93-008572b1f0ec/download733bec43a0bf5ade4d97db708e29b185MD53TEXTUsefulness_cutting_planes_hierarchical segmentation_CAS.pdf.txtUsefulness_cutting_planes_hierarchical segmentation_CAS.pdf.txtExtracted texttext/plain15502https://bonga.unisimon.edu.co/bitstreams/89adb232-053b-4a74-9db8-36ed9cc17ff9/download3e635de77aaf74554193c6f4964f53cbMD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain16032https://bonga.unisimon.edu.co/bitstreams/90204745-c5db-4c2f-85a0-59d8b4fc3b34/downloadbc4b80591ccb702d1e09a9f55a642383MD56THUMBNAILUsefulness_cutting_planes_hierarchical segmentation_CAS.pdf.jpgUsefulness_cutting_planes_hierarchical segmentation_CAS.pdf.jpgGenerated Thumbnailimage/jpeg1293https://bonga.unisimon.edu.co/bitstreams/6df9c06f-c18f-4245-a36e-9923a4e5d517/download47914e9d686c80a042b1ad3d41c30ad1MD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg3340https://bonga.unisimon.edu.co/bitstreams/d70805e3-6838-4e86-b581-e5e2fb67ac72/downloadfb8110f00038e72a3838f86a94791bf3MD5720.500.12442/5114oai:bonga.unisimon.edu.co:20.500.12442/51142024-08-14 21:53:05.313http://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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