A review of computational methods applied for identification and quantification of atherosclerotic plaques in images
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.eswa.2015.10.016 http://hdl.handle.net/11449/165014 |
Resumo: | Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its components in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed. (C) 2015 Elsevier Ltd. All rights reserved. |
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A review of computational methods applied for identification and quantification of atherosclerotic plaques in imagesStrokeMedical imagingImage analysisImage segmentationEvaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its components in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed. (C) 2015 Elsevier Ltd. All rights reserved.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)European Regional Development Funds (ERDF), through the Operational Programme 'Thematic Factors of Competitiveness'(COMPETE)Portuguese Funds, through the Fundacao para a Ciencia e a Tecnologia (FCT)Minist Educ Brazil, CAPES Fdn, BR-70040020 Brasilia, DF, BrazilUniv Estadual Paulista, BR-15054000 Sj Do Rio Preto, BrazilUniv Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, P-4200465 Oporto, PortugalUniv Estadual Paulista, BR-15054000 Sj Do Rio Preto, BrazilCAPES: 0543/13-6Portuguese Funds, through the Fundacao para a Ciencia e a Tecnologia (FCT): FCOMP-01-0124-FEDER-028160/PTDC/BBB- BMD/3088/2012Elsevier B.V.Minist Educ BrazilUniversidade Estadual Paulista (Unesp)Univ PortoJodas, Danilo SamuelPereira, Aledir Silveira [UNESP]Tavares, Joao Manuel R. S.2018-11-27T06:00:15Z2018-11-27T06:00:15Z2016-03-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-14application/pdfhttp://dx.doi.org/10.1016/j.eswa.2015.10.016Expert Systems With Applications. Oxford: Pergamon-elsevier Science Ltd, v. 46, p. 1-14, 2016.0957-4174http://hdl.handle.net/11449/16501410.1016/j.eswa.2015.10.016WOS:000367112400001WOS000367112400001.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengExpert Systems With Applications1,271info:eu-repo/semantics/openAccess2023-12-02T06:16:15Zoai:repositorio.unesp.br:11449/165014Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:19:39.246663Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
title |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
spellingShingle |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images Jodas, Danilo Samuel Stroke Medical imaging Image analysis Image segmentation |
title_short |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
title_full |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
title_fullStr |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
title_full_unstemmed |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
title_sort |
A review of computational methods applied for identification and quantification of atherosclerotic plaques in images |
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 |
Stroke Medical imaging Image analysis Image segmentation |
topic |
Stroke Medical imaging Image analysis Image segmentation |
description |
Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its components in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed. (C) 2015 Elsevier Ltd. All rights reserved. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-03-15 2018-11-27T06:00:15Z 2018-11-27T06:00:15Z |
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.eswa.2015.10.016 Expert Systems With Applications. Oxford: Pergamon-elsevier Science Ltd, v. 46, p. 1-14, 2016. 0957-4174 http://hdl.handle.net/11449/165014 10.1016/j.eswa.2015.10.016 WOS:000367112400001 WOS000367112400001.pdf |
url |
http://dx.doi.org/10.1016/j.eswa.2015.10.016 http://hdl.handle.net/11449/165014 |
identifier_str_mv |
Expert Systems With Applications. Oxford: Pergamon-elsevier Science Ltd, v. 46, p. 1-14, 2016. 0957-4174 10.1016/j.eswa.2015.10.016 WOS:000367112400001 WOS000367112400001.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Expert Systems With Applications 1,271 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-14 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_ |
1808129052415885312 |