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dc.contributor.author | Llobet Azpitarte, Rafael | es_ES |
dc.contributor.author | Pollán, Marina | es_ES |
dc.contributor.author | Antón Guirao, Joaquín | es_ES |
dc.contributor.author | Miranda-García, Josefa | es_ES |
dc.contributor.author | Casals el Busto, María | es_ES |
dc.contributor.author | Martinez Gomez, Inmaculada | es_ES |
dc.contributor.author | Ruiz Perales, Francisco | es_ES |
dc.contributor.author | Pérez Gómez, Beatriz | es_ES |
dc.contributor.author | Salas-Trejo, Dolores | es_ES |
dc.contributor.author | Perez-Cortes, Juan-Carlos | es_ES |
dc.date.accessioned | 2015-07-06T06:43:51Z | |
dc.date.available | 2015-07-06T06:43:51Z | |
dc.date.issued | 2014-09 | |
dc.identifier.issn | 0169-2607 | |
dc.identifier.issn | 1872-7565 | |
dc.identifier.uri | http://hdl.handle.net/10251/52701 | |
dc.description.abstract | The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved. | es_ES |
dc.description.sponsorship | This work was supported by research grants from Gent per Gent Fund (EDEMAC Project); Spain's Health Research Fund (Fondo de Investigacion Santiaria) (PI060386 & FIS PS09/00790); Spanish MICINN grants TIN2009-14205-C04-02 and Consolider-Ingenio 2010: MIPRCV (CSD2007-00018); Spanish Federation of Breast Cancer Patients (Federacion Espanola de Cancer de Mama) (FECMA 485 EPY 1170-10). The English revision of this paper was funded by the Universitat Politecnica de Valencia, Spain. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computer Methods and Programs in Biomedicine | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Mammographic density | es_ES |
dc.subject | Automated density assessment | es_ES |
dc.subject | Computer-aided diagnosis | es_ES |
dc.subject | Computer image analysis | es_ES |
dc.subject | Breast cancer risk | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.cmpb.2014.01.021 | |
dc.relation.projectID | info:eu-repo/grantAgreement/ISCIII//PI06%2F0386/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ISCIII//PS09%2F00790/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2009-14205-C04-02/ES/Tecnicas Interactivas Y Adaptativas Para Sistemas Automaticos De Reconocimiento, Aprendizaje Y Percepcion/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//CSD2007-00018/ES/Multimodal Intraction in Pattern Recognition and Computer Visionm/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FECMA//485 EPY 1170–10/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Área de Instituto de Ciencias de la Educación - Àrea de l'Institut de Ciències de l'Educació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors | es_ES |
dc.description.bibliographicCitation | Llobet Azpitarte, R.; Pollán, M.; Antón Guirao, J.; Miranda-García, J.; Casals El Busto, M.; Martinez Gomez, I.; Ruiz Perales, F.... (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine. 116(2):105-115. https://doi.org/10.1016/j.cmpb.2014.01.021 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.cmpb.2014.01.021 | es_ES |
dc.description.upvformatpinicio | 105 | es_ES |
dc.description.upvformatpfin | 115 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 116 | es_ES |
dc.description.issue | 2 | es_ES |
dc.relation.senia | 280635 | |
dc.contributor.funder | Ministerio de Educación y Ciencia | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Instituto de Salud Carlos III; Fondo de Investigaciones Sanitarias | es_ES |
dc.contributor.funder | Federación Española de Cáncer de Mama | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | Fundación Gent per Gent | es_ES |