Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a...

Descripción completa

Detalles Bibliográficos
Autores: Bosch Rué, Anna, Muñoz Pujol, Xavier, Oliver i Malagelada, Arnau, Martí Bonmatí, Joan
Tipo de recurso: artículo
Fecha de publicación:2006
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/2314
Acceso en línea:http://hdl.handle.net/10256/2314
Access Level:acceso abierto
Palabra clave:Diagnòstic per la imatge
Imatges -- Processament -- Tècniques digitals
Imatgeria mèdica – Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Image processing -- Digital techniques
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
id ES_e042de95bfbef72e94755260efdf7905
oai_identifier_str oai:recercat.cat:10256/2314
network_acronym_str ES
network_name_str España
repository_id_str
spelling Modeling and Classifying Breast Tissue Density in MammogramsBosch Rué, AnnaMuñoz Pujol, XavierOliver i Malagelada, ArnauMartí Bonmatí, JoanDiagnòstic per la imatgeImatges -- Processament -- Tècniques digitalsImatgeria mèdica – Processament -- Tècniques digitalsMama -- RadiografiaRadiografia mèdica -- Tècniques digitalsBreast -- RadiographyDiagnostic imagingImage processing -- Digital techniquesImaging systems in medicine -- Digital techniquesRadiography, Medical -- Digital techniquesWe present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposalIEEE2006info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10256/2314http://hdl.handle.net/10256/2314© IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, p. 1552-1558Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1109/CVPR.2006.188info:eu-repo/semantics/altIdentifier/issn/1063-6919info:eu-repo/semantics/altIdentifier/isbn/0-7695-2597-0Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:recercat.cat:10256/23142026-05-29T05:05:01Z
dc.title.none.fl_str_mv Modeling and Classifying Breast Tissue Density in Mammograms
title Modeling and Classifying Breast Tissue Density in Mammograms
spellingShingle Modeling and Classifying Breast Tissue Density in Mammograms
Bosch Rué, Anna
Diagnòstic per la imatge
Imatges -- Processament -- Tècniques digitals
Imatgeria mèdica – Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Image processing -- Digital techniques
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
title_short Modeling and Classifying Breast Tissue Density in Mammograms
title_full Modeling and Classifying Breast Tissue Density in Mammograms
title_fullStr Modeling and Classifying Breast Tissue Density in Mammograms
title_full_unstemmed Modeling and Classifying Breast Tissue Density in Mammograms
title_sort Modeling and Classifying Breast Tissue Density in Mammograms
dc.creator.none.fl_str_mv Bosch Rué, Anna
Muñoz Pujol, Xavier
Oliver i Malagelada, Arnau
Martí Bonmatí, Joan
author Bosch Rué, Anna
author_facet Bosch Rué, Anna
Muñoz Pujol, Xavier
Oliver i Malagelada, Arnau
Martí Bonmatí, Joan
author_role author
author2 Muñoz Pujol, Xavier
Oliver i Malagelada, Arnau
Martí Bonmatí, Joan
author2_role author
author
author
dc.subject.none.fl_str_mv Diagnòstic per la imatge
Imatges -- Processament -- Tècniques digitals
Imatgeria mèdica – Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Image processing -- Digital techniques
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
topic Diagnòstic per la imatge
Imatges -- Processament -- Tècniques digitals
Imatgeria mèdica – Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Image processing -- Digital techniques
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
description We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal
publishDate 2006
dc.date.none.fl_str_mv 2006
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/2314
http://hdl.handle.net/10256/2314
url http://hdl.handle.net/10256/2314
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/CVPR.2006.188
info:eu-repo/semantics/altIdentifier/issn/1063-6919
info:eu-repo/semantics/altIdentifier/isbn/0-7695-2597-0
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv © IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, p. 1552-1558
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422186193747968
score 15.812429

Similares en LA Referencia