Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
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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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 |
_version_ |
1752126288768794624 |