Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Ricardo
Data de Publicação: 2009
Outros Autores: Sanches, João
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/3939
Resumo: Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.
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spelling Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterizationLiver steatosisUltrasoundHepatic parenchymaHepatic vesiclesFat accumulationAutomatic diagnosticLiver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.This work was supported by Fundação para a Ciência e Tecnologia (ISR/IST plurianual funding) through the POS Conhecimento Program which includes FEDER funds.RCIPLRibeiro, RicardoSanches, João2014-11-19T12:47:54Z2009-102009-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/3939engRibeiro R, Sanches J. Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization. In Proceedings of RecPad 2009 - 15th edition of the Portuguese Conference on Pattern Recognition, Aveiro (Portugal), October 2009.info: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:RCAAP2023-08-03T09:45:17Zoai:repositorio.ipl.pt:10400.21/3939Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:13:28.420760Repositório 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 diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
title Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
spellingShingle Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
Ribeiro, Ricardo
Liver steatosis
Ultrasound
Hepatic parenchyma
Hepatic vesicles
Fat accumulation
Automatic diagnostic
title_short Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
title_full Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
title_fullStr Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
title_full_unstemmed Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
title_sort Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
author Ribeiro, Ricardo
author_facet Ribeiro, Ricardo
Sanches, João
author_role author
author2 Sanches, João
author2_role author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Ribeiro, Ricardo
Sanches, João
dc.subject.por.fl_str_mv Liver steatosis
Ultrasound
Hepatic parenchyma
Hepatic vesicles
Fat accumulation
Automatic diagnostic
topic Liver steatosis
Ultrasound
Hepatic parenchyma
Hepatic vesicles
Fat accumulation
Automatic diagnostic
description Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.
publishDate 2009
dc.date.none.fl_str_mv 2009-10
2009-10-01T00:00:00Z
2014-11-19T12:47:54Z
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://hdl.handle.net/10400.21/3939
url http://hdl.handle.net/10400.21/3939
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ribeiro R, Sanches J. Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization. In Proceedings of RecPad 2009 - 15th edition of the Portuguese Conference on Pattern Recognition, Aveiro (Portugal), October 2009.
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
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