Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/107795 |
Resumo: | The segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteries represents a crucial step towards the evaluation of cerebrovascular diseases. However, the automatic segmentation of the lumen is still a challenge due to the usual low quality of the images and the presence of elements that compromise the accuracy of the results. In this article, we describe a fully automatic method to identify the location of the lumen in MR images of the carotid artery. A circularity index is used to assess the roundness of the regions identified by the K-means algorithm in order to obtain the one with the maximum value, i.e. the potential lumen region. Then, an active contour algorithm is employed to refine the boundary of the region found. The method achieved a maximum Dice coefficient of 0.91 +/- 0.04 and 0.74 +/- 0.16 in 181 postcontrast 3D-T1-weighted and 181 proton density-weighted MR images, respectively. Therefore, the method seems to be promising for identifying the correct location of the lumen in MR images. |
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Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid ArteryCiências Tecnológicas, Ciências da engenharia e tecnologiasTechnological sciences, Engineering and technologyThe segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteries represents a crucial step towards the evaluation of cerebrovascular diseases. However, the automatic segmentation of the lumen is still a challenge due to the usual low quality of the images and the presence of elements that compromise the accuracy of the results. In this article, we describe a fully automatic method to identify the location of the lumen in MR images of the carotid artery. A circularity index is used to assess the roundness of the regions identified by the K-means algorithm in order to obtain the one with the maximum value, i.e. the potential lumen region. Then, an active contour algorithm is employed to refine the boundary of the region found. The method achieved a maximum Dice coefficient of 0.91 +/- 0.04 and 0.74 +/- 0.16 in 181 postcontrast 3D-T1-weighted and 181 proton density-weighted MR images, respectively. Therefore, the method seems to be promising for identifying the correct location of the lumen in MR images.20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/107795eng10.1007/978-3-319-68195-5_10Danilo Samuel JodasAledir Silveira PereiraJoão Manuel R. S. Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T12:47:14Zoai:repositorio-aberto.up.pt:10216/107795Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:26:42.377605Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
title |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
spellingShingle |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery Danilo Samuel Jodas Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
title_short |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
title_full |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
title_fullStr |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
title_full_unstemmed |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
title_sort |
Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery |
author |
Danilo Samuel Jodas |
author_facet |
Danilo Samuel Jodas Aledir Silveira Pereira João Manuel R. S. Tavares |
author_role |
author |
author2 |
Aledir Silveira Pereira João Manuel R. S. Tavares |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Danilo Samuel Jodas Aledir Silveira Pereira João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
topic |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
description |
The segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteries represents a crucial step towards the evaluation of cerebrovascular diseases. However, the automatic segmentation of the lumen is still a challenge due to the usual low quality of the images and the presence of elements that compromise the accuracy of the results. In this article, we describe a fully automatic method to identify the location of the lumen in MR images of the carotid artery. A circularity index is used to assess the roundness of the regions identified by the K-means algorithm in order to obtain the one with the maximum value, i.e. the potential lumen region. Then, an active contour algorithm is employed to refine the boundary of the region found. The method achieved a maximum Dice coefficient of 0.91 +/- 0.04 and 0.74 +/- 0.16 in 181 postcontrast 3D-T1-weighted and 181 proton density-weighted MR images, respectively. Therefore, the method seems to be promising for identifying the correct location of the lumen in MR images. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/107795 |
url |
https://hdl.handle.net/10216/107795 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/978-3-319-68195-5_10 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135573857796096 |