Unsupervised Breast Masses Classification Through Optimum-Path Forest
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/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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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 |
|
_version_ |
1808129035615600640 |