Automatic segmentation of digital images applied in cardiac medical images
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PAHCE.2010.5474606 http://hdl.handle.net/11449/71782 |
Resumo: | The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE. |
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Automatic segmentation of digital images applied in cardiac medical imagesCardiac imagensSegmentationThresholdingAutomatic segmentationsDigital imageDigital image processingInitial stagesMaximum entropyMedical imagesMultilevel thresholdingSegmentation methodsDigital image storageFeature extractionGraphic methodsHealth careHeartMedical imagingImage segmentationThe digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE.Faculdade de Tecnologia de São José do Rio Preto, São José do Rio Preto, SPUniversidade Estadual Paulista Departamento de Estatística, Matemática Aplicada e Computação, Rio Claro, SPFaculdade de Medicina de São José do Rio Preto, São José do Rio Preto, SPUniversidade Estadual Paulista Departamento de Estatística, Matemática Aplicada e Computação, Rio Claro, SPFaculdade de Tecnologia de São José do Rio PretoUniversidade Estadual Paulista (Unesp)Faculdade de Medicina de São José do Rio Preto (FAMERP)Peres, F. A.Oliveira, F. R.Neves, L. A. [UNESP]Godoy, M. F.2014-05-27T11:24:44Z2014-05-27T11:24:44Z2010-07-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject38-42http://dx.doi.org/10.1109/PAHCE.2010.5474606Pan American Health Care Exchanges, PAHCE 2010, p. 38-42.http://hdl.handle.net/11449/7178210.1109/PAHCE.2010.54746062-s2.0-77954253661Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPan American Health Care Exchanges, PAHCE 2010info:eu-repo/semantics/openAccess2021-10-23T21:37:44Zoai:repositorio.unesp.br:11449/71782Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:16:01.551282Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automatic segmentation of digital images applied in cardiac medical images |
title |
Automatic segmentation of digital images applied in cardiac medical images |
spellingShingle |
Automatic segmentation of digital images applied in cardiac medical images Peres, F. A. Cardiac imagens Segmentation Thresholding Automatic segmentations Digital image Digital image processing Initial stages Maximum entropy Medical images Multilevel thresholding Segmentation methods Digital image storage Feature extraction Graphic methods Health care Heart Medical imaging Image segmentation |
title_short |
Automatic segmentation of digital images applied in cardiac medical images |
title_full |
Automatic segmentation of digital images applied in cardiac medical images |
title_fullStr |
Automatic segmentation of digital images applied in cardiac medical images |
title_full_unstemmed |
Automatic segmentation of digital images applied in cardiac medical images |
title_sort |
Automatic segmentation of digital images applied in cardiac medical images |
author |
Peres, F. A. |
author_facet |
Peres, F. A. Oliveira, F. R. Neves, L. A. [UNESP] Godoy, M. F. |
author_role |
author |
author2 |
Oliveira, F. R. Neves, L. A. [UNESP] Godoy, M. F. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Faculdade de Tecnologia de São José do Rio Preto Universidade Estadual Paulista (Unesp) Faculdade de Medicina de São José do Rio Preto (FAMERP) |
dc.contributor.author.fl_str_mv |
Peres, F. A. Oliveira, F. R. Neves, L. A. [UNESP] Godoy, M. F. |
dc.subject.por.fl_str_mv |
Cardiac imagens Segmentation Thresholding Automatic segmentations Digital image Digital image processing Initial stages Maximum entropy Medical images Multilevel thresholding Segmentation methods Digital image storage Feature extraction Graphic methods Health care Heart Medical imaging Image segmentation |
topic |
Cardiac imagens Segmentation Thresholding Automatic segmentations Digital image Digital image processing Initial stages Maximum entropy Medical images Multilevel thresholding Segmentation methods Digital image storage Feature extraction Graphic methods Health care Heart Medical imaging Image segmentation |
description |
The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-07-09 2014-05-27T11:24:44Z 2014-05-27T11:24:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/PAHCE.2010.5474606 Pan American Health Care Exchanges, PAHCE 2010, p. 38-42. http://hdl.handle.net/11449/71782 10.1109/PAHCE.2010.5474606 2-s2.0-77954253661 |
url |
http://dx.doi.org/10.1109/PAHCE.2010.5474606 http://hdl.handle.net/11449/71782 |
identifier_str_mv |
Pan American Health Care Exchanges, PAHCE 2010, p. 38-42. 10.1109/PAHCE.2010.5474606 2-s2.0-77954253661 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pan American Health Care Exchanges, PAHCE 2010 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
38-42 |
dc.source.none.fl_str_mv |
Scopus 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_ |
1808128914224054272 |