Lumen segmentation in magnetic resonance images of the carotid artery
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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.2016.10.021 http://hdl.handle.net/11449/165396 |
Resumo: | Investigation of the carotid artery plays an important role in the diagnosis of cerebrovascular events. Segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images is the first step towards evaluating any possible cardiovascular diseases like atherosclerosis. However, the automatic segmentation of the lumen is still a challenge due to the low quality of the images and the presence of other elements such as stenosis and malformations that compromise the accuracy of the results. In this article, a method to identify the location of the lumen without user interaction is presented. The proposed method uses the modified mean roundness to calculate the circularity index of the regions identified by the K-means algorithm and return the one with the maximum value, i.e. the potential lumen region. Then, an active contour is employed to refine the boundary of this region. The method achieved an average Dice coefficient of 0.78 +/- 0.14 and 0.61 +/- 0.21 in 181 3D-T1-weighted and 181 proton density-weighted MR images, respectively. The results show that this method is promising for the correct identification and location of the lumen even in images corrupted by noise. |
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Lumen segmentation in magnetic resonance images of the carotid arteryMagnetic Resonance ImagingK-means algorithmDeformable modelSubtractive clusteringCircularity indexInvestigation of the carotid artery plays an important role in the diagnosis of cerebrovascular events. Segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images is the first step towards evaluating any possible cardiovascular diseases like atherosclerosis. However, the automatic segmentation of the lumen is still a challenge due to the low quality of the images and the presence of other elements such as stenosis and malformations that compromise the accuracy of the results. In this article, a method to identify the location of the lumen without user interaction is presented. The proposed method uses the modified mean roundness to calculate the circularity index of the regions identified by the K-means algorithm and return the one with the maximum value, i.e. the potential lumen region. Then, an active contour is employed to refine the boundary of this region. The method achieved an average Dice coefficient of 0.78 +/- 0.14 and 0.61 +/- 0.21 in 181 3D-T1-weighted and 181 proton density-weighted MR images, respectively. The results show that this method is promising for the correct identification and location of the lumen even in images corrupted by noise.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Project - SciTech - Science and Technology for Competitive and Sustainable Industries - by Programa Operacional Regional do Norte (NORTE) through Fundo Europeu de Desenvolvimento Regional (FEDER)Minist Educ Brazil, CAPES Fdn, BR-70040020 Brasilia, DF, BrazilUniv 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 Oporto, PortugalUniv Estadual Paulista, Rua Cristovao Colombo,2265, BR-15054000 S J Do Rio Preto, BrazilCAPES: 0543/13-6Project - SciTech - Science and Technology for Competitive and Sustainable Industries - by Programa Operacional Regional do Norte (NORTE) through Fundo Europeu de Desenvolvimento Regional (FEDER): NORTE-01-0145-FEDER-000022Elsevier B.V.Minist Educ BrazilUniversidade Estadual Paulista (Unesp)Univ PortoJodas, Danilo SamuelPereira, Aledir Silveira [UNESP]Tavares, Joao Manuel R. S.2018-11-28T00:18:33Z2018-11-28T00:18:33Z2016-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article233-242application/pdfhttp://dx.doi.org/10.1016/j.compbiomed.2016.10.021Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 79, p. 233-242, 2016.0010-4825http://hdl.handle.net/11449/16539610.1016/j.compbiomed.2016.10.021WOS:000389294700024WOS000389294700024.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers In Biology And Medicine0,591info:eu-repo/semantics/openAccess2024-01-11T06:30:46Zoai:repositorio.unesp.br:11449/165396Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-11T06:30:46Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Lumen segmentation in magnetic resonance images of the carotid artery |
title |
Lumen segmentation in magnetic resonance images of the carotid artery |
spellingShingle |
Lumen segmentation in magnetic resonance images of the carotid artery Jodas, Danilo Samuel Magnetic Resonance Imaging K-means algorithm Deformable model Subtractive clustering Circularity index |
title_short |
Lumen segmentation in magnetic resonance images of the carotid artery |
title_full |
Lumen segmentation in magnetic resonance images of the carotid artery |
title_fullStr |
Lumen segmentation in magnetic resonance images of the carotid artery |
title_full_unstemmed |
Lumen segmentation in magnetic resonance images of the carotid artery |
title_sort |
Lumen segmentation in magnetic resonance images of the carotid artery |
author |
Jodas, Danilo Samuel |
author_facet |
Jodas, Danilo Samuel Pereira, Aledir Silveira [UNESP] Tavares, Joao Manuel R. S. |
author_role |
author |
author2 |
Pereira, Aledir Silveira [UNESP] Tavares, Joao Manuel R. S. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Minist Educ Brazil Universidade Estadual Paulista (Unesp) Univ Porto |
dc.contributor.author.fl_str_mv |
Jodas, Danilo Samuel Pereira, Aledir Silveira [UNESP] Tavares, Joao Manuel R. S. |
dc.subject.por.fl_str_mv |
Magnetic Resonance Imaging K-means algorithm Deformable model Subtractive clustering Circularity index |
topic |
Magnetic Resonance Imaging K-means algorithm Deformable model Subtractive clustering Circularity index |
description |
Investigation of the carotid artery plays an important role in the diagnosis of cerebrovascular events. Segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images is the first step towards evaluating any possible cardiovascular diseases like atherosclerosis. However, the automatic segmentation of the lumen is still a challenge due to the low quality of the images and the presence of other elements such as stenosis and malformations that compromise the accuracy of the results. In this article, a method to identify the location of the lumen without user interaction is presented. The proposed method uses the modified mean roundness to calculate the circularity index of the regions identified by the K-means algorithm and return the one with the maximum value, i.e. the potential lumen region. Then, an active contour is employed to refine the boundary of this region. The method achieved an average Dice coefficient of 0.78 +/- 0.14 and 0.61 +/- 0.21 in 181 3D-T1-weighted and 181 proton density-weighted MR images, respectively. The results show that this method is promising for the correct identification and location of the lumen even in images corrupted by noise. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-01 2018-11-28T00:18:33Z 2018-11-28T00:18:33Z |
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.2016.10.021 Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 79, p. 233-242, 2016. 0010-4825 http://hdl.handle.net/11449/165396 10.1016/j.compbiomed.2016.10.021 WOS:000389294700024 WOS000389294700024.pdf |
url |
http://dx.doi.org/10.1016/j.compbiomed.2016.10.021 http://hdl.handle.net/11449/165396 |
identifier_str_mv |
Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 79, p. 233-242, 2016. 0010-4825 10.1016/j.compbiomed.2016.10.021 WOS:000389294700024 WOS000389294700024.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers In Biology And Medicine 0,591 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
233-242 application/pdf |
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_ |
1803047320599658496 |