Automatic segmentation of digital images applied in cardiac medical images

Detalhes bibliográficos
Autor(a) principal: Peres, F. A.
Data de Publicação: 2010
Outros Autores: Oliveira, F. R., Neves, L. A. [UNESP], Godoy, M. F.
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.
id UNSP_e696dfe5c032f06fb372d6e70ea52df0
oai_identifier_str oai:repositorio.unesp.br:11449/71782
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling 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