Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.compbiomed.2020.103901 http://hdl.handle.net/11449/197881 |
Resumo: | Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 +/- 0.11, 2.70 +/- 1.69 pixels, 2.79 +/- 1.89 pixels and 3.44 +/- 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges. |
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Repositório Institucional da UNESP |
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Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance imagesMedical imagingMagnetic resonance imagingImage segmentationSnake modelGray-weighted distanceSegmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 +/- 0.11, 2.70 +/- 1.69 pixels, 2.79 +/- 1.89 pixels and 3.44 +/- 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Programa Operacional Regional do Norte'' (NORTE2020), through Fundo Europeu de Desenvolvimento Regional'' (FEDER)Minist Educ Brazil, CAPES Fdn, BR-70040020 Brasilia, DF, BrazilCtr Hosp Sao Joao, Serv Neurorradiol, IFE Neurorradiol, Alameda Prof Hernani Monteiro, P-4200319 Porto, PortugalCtr Hosp Sao Joao, Serv Neurorradiol, AH Neurorradiol, Alameda Prof Hernani Monteiro, P-4200319 Porto, PortugalUniv Estadual Paulista, Rua Cristovao Colombo 2265, BR-15054000 S J Do Rio Preto, BrazilUniv Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Rua Dr Roberto Frias S-N, P-4200465 Porto, PortugalUniv Estadual Paulista, Rua Cristovao Colombo 2265, BR-15054000 S J Do Rio Preto, BrazilCAPES: 0543/13-6Programa Operacional Regional do Norte'' (NORTE2020), through Fundo Europeu de Desenvolvimento Regional'' (FEDER): NORTE-01-0145-FEDER-000022Elsevier B.V.Minist Educ BrazilCtr Hosp Sao JoaoUniversidade Estadual Paulista (Unesp)Univ PortoJodas, Danilo SamuelMonteiro da Costa, Maria FranciscaParreira, Tiago A. A.Pereira, Aledir Silveira [UNESP]Tavares, Joao Manuel R. S.2020-12-11T23:14:02Z2020-12-11T23:14:02Z2020-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18http://dx.doi.org/10.1016/j.compbiomed.2020.103901Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 123, 18 p., 2020.0010-4825http://hdl.handle.net/11449/19788110.1016/j.compbiomed.2020.103901WOS:000558010800031Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers In Biology And Medicineinfo:eu-repo/semantics/openAccess2021-10-22T21:15:48Zoai:repositorio.unesp.br:11449/197881Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:18:36.519996Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
title |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
spellingShingle |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images Jodas, Danilo Samuel Medical imaging Magnetic resonance imaging Image segmentation Snake model Gray-weighted distance |
title_short |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
title_full |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
title_fullStr |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
title_full_unstemmed |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
title_sort |
Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images |
author |
Jodas, Danilo Samuel |
author_facet |
Jodas, Danilo Samuel Monteiro da Costa, Maria Francisca Parreira, Tiago A. A. Pereira, Aledir Silveira [UNESP] Tavares, Joao Manuel R. S. |
author_role |
author |
author2 |
Monteiro da Costa, Maria Francisca Parreira, Tiago A. A. Pereira, Aledir Silveira [UNESP] Tavares, Joao Manuel R. S. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Minist Educ Brazil Ctr Hosp Sao Joao Universidade Estadual Paulista (Unesp) Univ Porto |
dc.contributor.author.fl_str_mv |
Jodas, Danilo Samuel Monteiro da Costa, Maria Francisca Parreira, Tiago A. A. Pereira, Aledir Silveira [UNESP] Tavares, Joao Manuel R. S. |
dc.subject.por.fl_str_mv |
Medical imaging Magnetic resonance imaging Image segmentation Snake model Gray-weighted distance |
topic |
Medical imaging Magnetic resonance imaging Image segmentation Snake model Gray-weighted distance |
description |
Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 +/- 0.11, 2.70 +/- 1.69 pixels, 2.79 +/- 1.89 pixels and 3.44 +/- 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-11T23:14:02Z 2020-12-11T23:14:02Z 2020-08-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.compbiomed.2020.103901 Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 123, 18 p., 2020. 0010-4825 http://hdl.handle.net/11449/197881 10.1016/j.compbiomed.2020.103901 WOS:000558010800031 |
url |
http://dx.doi.org/10.1016/j.compbiomed.2020.103901 http://hdl.handle.net/11449/197881 |
identifier_str_mv |
Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 123, 18 p., 2020. 0010-4825 10.1016/j.compbiomed.2020.103901 WOS:000558010800031 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers In Biology And Medicine |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
18 |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128632360534016 |