Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images

Detalhes bibliográficos
Autor(a) principal: Arthur,Angélica Moises
Data de Publicação: 2017
Outros Autores: Arthur,Rangel, Silva,Alexandre Gonçalves, Fouto,Marina Silva, Iano,Yuzo, Faria,Jacqueline Mendonça Lopes de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research on Biomedical Engineering (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400344
Resumo: Abstract Introduction A new method for segmenting and quantifying the macular area based on morphological alternating sequential filtering (ASF) is proposed. Previous studies show that persons with diabetes present alterations in the foveal avascular zone (FAZ) prior to the appearance of retinopathy. Thus, a proper characterization of FAZ using a method of automatic classification and prediction is a supportive and complementary tool for medical evaluation of the macular region, and may be useful for possible early treatment of eye diseases in persons without diabetic retinopathy. Methods We obtained high-resolution retinal images using a non-invasive functional imaging system called Retinal Function Imager to generate a series of combined capillary perfusion maps. We filtered sequentially the macular images to reduce the complexity by ASF. Then we segmented the FAZ using watershed transform from an automatic selection of markers. Using Hu’s moment invariants as a descriptor, we can automatically classify and categorize each FAZ. Results The FAZ differences between non-diabetic volunteers and diabetic subjects were automatically distinguished by the proposed system with an accuracy of 81%. Conclusion This is an innovative method to classify FAZ using a fully automatic algorithm for segmentation (based on morphological operators) and for the classification (based on descriptor formed by Hu’s moments) despite the presence of edema or other structures. This is an alternative tool for eye exams, which may contribute to the analysis and evaluation of FAZ morphology, promoting the prevention of macular impairment in diabetics without retinopathy.
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spelling Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal imagesCapillary perfusion mappingFoveal avascular zoneAlternating sequential filteringAutomatic classification of diabetesAbstract Introduction A new method for segmenting and quantifying the macular area based on morphological alternating sequential filtering (ASF) is proposed. Previous studies show that persons with diabetes present alterations in the foveal avascular zone (FAZ) prior to the appearance of retinopathy. Thus, a proper characterization of FAZ using a method of automatic classification and prediction is a supportive and complementary tool for medical evaluation of the macular region, and may be useful for possible early treatment of eye diseases in persons without diabetic retinopathy. Methods We obtained high-resolution retinal images using a non-invasive functional imaging system called Retinal Function Imager to generate a series of combined capillary perfusion maps. We filtered sequentially the macular images to reduce the complexity by ASF. Then we segmented the FAZ using watershed transform from an automatic selection of markers. Using Hu’s moment invariants as a descriptor, we can automatically classify and categorize each FAZ. Results The FAZ differences between non-diabetic volunteers and diabetic subjects were automatically distinguished by the proposed system with an accuracy of 81%. Conclusion This is an innovative method to classify FAZ using a fully automatic algorithm for segmentation (based on morphological operators) and for the classification (based on descriptor formed by Hu’s moments) despite the presence of edema or other structures. This is an alternative tool for eye exams, which may contribute to the analysis and evaluation of FAZ morphology, promoting the prevention of macular impairment in diabetics without retinopathy.Sociedade Brasileira de Engenharia Biomédica2017-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400344Research on Biomedical Engineering v.33 n.4 2017reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.01417info:eu-repo/semantics/openAccessArthur,Angélica MoisesArthur,RangelSilva,Alexandre GonçalvesFouto,Marina SilvaIano,YuzoFaria,Jacqueline Mendonça Lopes deeng2018-01-09T00:00:00Zoai:scielo:S2446-47402017000400344Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-01-09T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false
dc.title.none.fl_str_mv Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
title Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
spellingShingle Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
Arthur,Angélica Moises
Capillary perfusion mapping
Foveal avascular zone
Alternating sequential filtering
Automatic classification of diabetes
title_short Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
title_full Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
title_fullStr Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
title_full_unstemmed Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
title_sort Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
author Arthur,Angélica Moises
author_facet Arthur,Angélica Moises
Arthur,Rangel
Silva,Alexandre Gonçalves
Fouto,Marina Silva
Iano,Yuzo
Faria,Jacqueline Mendonça Lopes de
author_role author
author2 Arthur,Rangel
Silva,Alexandre Gonçalves
Fouto,Marina Silva
Iano,Yuzo
Faria,Jacqueline Mendonça Lopes de
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Arthur,Angélica Moises
Arthur,Rangel
Silva,Alexandre Gonçalves
Fouto,Marina Silva
Iano,Yuzo
Faria,Jacqueline Mendonça Lopes de
dc.subject.por.fl_str_mv Capillary perfusion mapping
Foveal avascular zone
Alternating sequential filtering
Automatic classification of diabetes
topic Capillary perfusion mapping
Foveal avascular zone
Alternating sequential filtering
Automatic classification of diabetes
description Abstract Introduction A new method for segmenting and quantifying the macular area based on morphological alternating sequential filtering (ASF) is proposed. Previous studies show that persons with diabetes present alterations in the foveal avascular zone (FAZ) prior to the appearance of retinopathy. Thus, a proper characterization of FAZ using a method of automatic classification and prediction is a supportive and complementary tool for medical evaluation of the macular region, and may be useful for possible early treatment of eye diseases in persons without diabetic retinopathy. Methods We obtained high-resolution retinal images using a non-invasive functional imaging system called Retinal Function Imager to generate a series of combined capillary perfusion maps. We filtered sequentially the macular images to reduce the complexity by ASF. Then we segmented the FAZ using watershed transform from an automatic selection of markers. Using Hu’s moment invariants as a descriptor, we can automatically classify and categorize each FAZ. Results The FAZ differences between non-diabetic volunteers and diabetic subjects were automatically distinguished by the proposed system with an accuracy of 81%. Conclusion This is an innovative method to classify FAZ using a fully automatic algorithm for segmentation (based on morphological operators) and for the classification (based on descriptor formed by Hu’s moments) despite the presence of edema or other structures. This is an alternative tool for eye exams, which may contribute to the analysis and evaluation of FAZ morphology, promoting the prevention of macular impairment in diabetics without retinopathy.
publishDate 2017
dc.date.none.fl_str_mv 2017-10-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400344
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400344
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2446-4740.01417
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
dc.source.none.fl_str_mv Research on Biomedical Engineering v.33 n.4 2017
reponame:Research on Biomedical Engineering (Online)
instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron:SBEB
instname_str Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron_str SBEB
institution SBEB
reponame_str Research on Biomedical Engineering (Online)
collection Research on Biomedical Engineering (Online)
repository.name.fl_str_mv Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)
repository.mail.fl_str_mv ||rbe@rbejournal.org
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