A review of computational methods applied for identification and quantification of atherosclerotic plaques in images

Detalhes bibliográficos
Autor(a) principal: Jodas, Danilo Samuel
Data de Publicação: 2016
Outros Autores: 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.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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spelling 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
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