Unsupervised Breast Masses Classification Through Optimum-Path Forest

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Patricia. B. [UNESP]
Data de Publicação: 2015
Outros Autores: Passos, Leandro. A. [UNESP], Silva, Luis. A. da [UNESP], Costa, Kelton A. P. da [UNESP], Papa, Joao P. [UNESP], Romero, Roseli A. F., Traina, C., Rodrigues, P. P., Kane, B., Marques, PMD, Traina, AJM
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/CBMS.2015.53
http://hdl.handle.net/11449/165052
Resumo: Computer-Aided Diagnosis (CAD) can be divided into two main categories : CADe (Computer-Aided Detection), which is focused on the detection of structures of interest, as well as to assist radiologists to find out signals of interest that might be hidden to human vision; and the CADx (ComputerAided Diagnosis), which works as a second observer, being responsible to give an opinion on a specific lesion. In CADe -based systems, the identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest. The main contribution of this study is to introduce the unsupervised classifier Optimum-Path Forest to identify breast masses, and to evaluate its performance against with two other unsupervised techniques (Gaussian Mixture Model and k-Means) using texture features from images obtained from a private dataset composed by 120 images with and without the presence of masses.
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spelling Unsupervised Breast Masses Classification Through Optimum-Path ForestOptimum-Path ForesBreast massesMammographyComputer-Aided Diagnosis (CAD) can be divided into two main categories : CADe (Computer-Aided Detection), which is focused on the detection of structures of interest, as well as to assist radiologists to find out signals of interest that might be hidden to human vision; and the CADx (ComputerAided Diagnosis), which works as a second observer, being responsible to give an opinion on a specific lesion. In CADe -based systems, the identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest. The main contribution of this study is to introduce the unsupervised classifier Optimum-Path Forest to identify breast masses, and to evaluate its performance against with two other unsupervised techniques (Gaussian Mixture Model and k-Means) using texture features from images obtained from a private dataset composed by 120 images with and without the presence of masses.Sao Paulo State Univ, Dept Comp, Sao Paulo, BrazilUniv Sao Paulo, Dept Comp Sci, Sao Paulo, BrazilSao Paulo State Univ, Dept Comp, Sao Paulo, BrazilIeee Computer SocUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Ribeiro, Patricia. B. [UNESP]Passos, Leandro. A. [UNESP]Silva, Luis. A. da [UNESP]Costa, Kelton A. P. da [UNESP]Papa, Joao P. [UNESP]Romero, Roseli A. F.Traina, C.Rodrigues, P. P.Kane, B.Marques, PMDTraina, AJM2018-11-27T08:16:27Z2018-11-27T08:16:27Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject238-243http://dx.doi.org/10.1109/CBMS.2015.532015 Ieee 28th International Symposium On Computer-based Medical Systems (cbms). Los Alamitos: Ieee Computer Soc, p. 238-243, 2015.1063-7125http://hdl.handle.net/11449/16505210.1109/CBMS.2015.53WOS:000369099700050Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Ieee 28th International Symposium On Computer-based Medical Systems (cbms)info:eu-repo/semantics/openAccess2024-04-23T16:11:26Zoai:repositorio.unesp.br:11449/165052Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:13:06.560062Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Unsupervised Breast Masses Classification Through Optimum-Path Forest
title Unsupervised Breast Masses Classification Through Optimum-Path Forest
spellingShingle Unsupervised Breast Masses Classification Through Optimum-Path Forest
Ribeiro, Patricia. B. [UNESP]
Optimum-Path Fores
Breast masses
Mammography
title_short Unsupervised Breast Masses Classification Through Optimum-Path Forest
title_full Unsupervised Breast Masses Classification Through Optimum-Path Forest
title_fullStr Unsupervised Breast Masses Classification Through Optimum-Path Forest
title_full_unstemmed Unsupervised Breast Masses Classification Through Optimum-Path Forest
title_sort Unsupervised Breast Masses Classification Through Optimum-Path Forest
author Ribeiro, Patricia. B. [UNESP]
author_facet Ribeiro, Patricia. B. [UNESP]
Passos, Leandro. A. [UNESP]
Silva, Luis. A. da [UNESP]
Costa, Kelton A. P. da [UNESP]
Papa, Joao P. [UNESP]
Romero, Roseli A. F.
Traina, C.
Rodrigues, P. P.
Kane, B.
Marques, PMD
Traina, AJM
author_role author
author2 Passos, Leandro. A. [UNESP]
Silva, Luis. A. da [UNESP]
Costa, Kelton A. P. da [UNESP]
Papa, Joao P. [UNESP]
Romero, Roseli A. F.
Traina, C.
Rodrigues, P. P.
Kane, B.
Marques, PMD
Traina, AJM
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Ribeiro, Patricia. B. [UNESP]
Passos, Leandro. A. [UNESP]
Silva, Luis. A. da [UNESP]
Costa, Kelton A. P. da [UNESP]
Papa, Joao P. [UNESP]
Romero, Roseli A. F.
Traina, C.
Rodrigues, P. P.
Kane, B.
Marques, PMD
Traina, AJM
dc.subject.por.fl_str_mv Optimum-Path Fores
Breast masses
Mammography
topic Optimum-Path Fores
Breast masses
Mammography
description Computer-Aided Diagnosis (CAD) can be divided into two main categories : CADe (Computer-Aided Detection), which is focused on the detection of structures of interest, as well as to assist radiologists to find out signals of interest that might be hidden to human vision; and the CADx (ComputerAided Diagnosis), which works as a second observer, being responsible to give an opinion on a specific lesion. In CADe -based systems, the identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest. The main contribution of this study is to introduce the unsupervised classifier Optimum-Path Forest to identify breast masses, and to evaluate its performance against with two other unsupervised techniques (Gaussian Mixture Model and k-Means) using texture features from images obtained from a private dataset composed by 120 images with and without the presence of masses.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2018-11-27T08:16:27Z
2018-11-27T08:16:27Z
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/CBMS.2015.53
2015 Ieee 28th International Symposium On Computer-based Medical Systems (cbms). Los Alamitos: Ieee Computer Soc, p. 238-243, 2015.
1063-7125
http://hdl.handle.net/11449/165052
10.1109/CBMS.2015.53
WOS:000369099700050
url http://dx.doi.org/10.1109/CBMS.2015.53
http://hdl.handle.net/11449/165052
identifier_str_mv 2015 Ieee 28th International Symposium On Computer-based Medical Systems (cbms). Los Alamitos: Ieee Computer Soc, p. 238-243, 2015.
1063-7125
10.1109/CBMS.2015.53
WOS:000369099700050
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2015 Ieee 28th International Symposium On Computer-based Medical Systems (cbms)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 238-243
dc.publisher.none.fl_str_mv Ieee Computer Soc
publisher.none.fl_str_mv Ieee Computer Soc
dc.source.none.fl_str_mv Web of Science
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
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