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

Detalhes bibliográficos
Autor(a) principal: Jodas, Danilo Samuel
Data de Publicação: 2020
Outros Autores: Monteiro da Costa, Maria Francisca, Parreira, Tiago A. A., Pereira, Aledir Silveira [UNESP], Tavares, Joao Manuel R. S.
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.
id UNSP_f075f146b6e392b4c015a34dbd81cbad
oai_identifier_str oai:repositorio.unesp.br:11449/197881
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling 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