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dc.contributor.authorPavan, Ana L. M. [UNESP]
dc.contributor.authorVacavant, Antoine
dc.contributor.authorAlves, Allan F. F. [UNESP]
dc.contributor.authorTrindade, Andre P. [UNESP]
dc.contributor.authorPina, Diana R. de [UNESP]
dc.contributor.authorLhotska, L.
dc.contributor.authorSukupova, L.
dc.contributor.authorLackovic, I
dc.contributor.authorIbbott, G. S.
dc.date.accessioned2019-10-04T12:32:37Z
dc.date.available2019-10-04T12:32:37Z
dc.date.issued2019-01-01
dc.identifierhttp://dx.doi.org/10.1007/978-981-10-9035-6_27
dc.identifier.citationWorld Congress On Medical Physics And Biomedical Engineering 2018, Vol 1. New York: Springer, v. 68, n. 1, p. 151-154, 2019.
dc.identifier.issn1680-0737
dc.identifier.urihttp://hdl.handle.net/11449/185086
dc.description.abstractMammography is a worldwide image modality used to diagnose breast cancer and can be used to measure breast density (BD). In clinical routine, radiologist perform image evaluations through BIRADS assessment. However, this method has inter and intraindividual variability. An automatic method to measure BD could relieve radiologist's workload by providing a first aid opinion. However, pectoral muscle (PM) is a high density tissue, with the same imaging characteristics as fibroglandular tissues, which makes its automatic detection a challenging task. The aim of this work is to develop an automatic algorithm to segment and extract PM in digital mammograms. A hybrid methodology has been developed using Hough transform, to find the edge of the PM, and active contour, to segment PM muscle. Seed of active contour is applied automatically in the edge of PM found by Hough transform. An experienced radiologist manually performed the PM segmentation. Manual and automatic methods were compared using the Jaccard index and Bland-Altman statistics. The comparison between methods presented a Jaccard similarity coefficient greater than 90% for all analyzed images. The Bland-Altman statistics compared the segmented PM area and showed agreement between both methods within 95% confidence interval. The method proved to be accurate and robust, segmenting rapid and free of intra and inter-observer variability.en
dc.format.extent151-154
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofWorld Congress On Medical Physics And Biomedical Engineering 2018, Vol 1
dc.sourceWeb of Science
dc.subjectMammography
dc.subjectPectoral muscle
dc.subjectHough transform and active contour
dc.titleAutomatic Identification and Extraction of Pectoral Muscle in Digital Mammographyen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Clermont Auvergne
dc.description.affiliationSao Paulo State Univ, Biosci Inst Botucatu, Dept Phys & Biophys, Dist Rubiao Jr S-N, BR-18618000 Botucatu, SP, Brazil
dc.description.affiliationUniv Clermont Auvergne, CNRS, SIGMA, Dept Inst Pascal,UCA,UMR 6602, F-63171 Aubiere, France
dc.description.affiliationUniv Estadual Paulista, Botucatu Med Sch, Dept Trop Dis & Diagnost Imaging, Dist Rubiao Jr S-N, BR-18618000 Botucatu, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Biosci Inst Botucatu, Dept Phys & Biophys, Dist Rubiao Jr S-N, BR-18618000 Botucatu, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Botucatu Med Sch, Dept Trop Dis & Diagnost Imaging, Dist Rubiao Jr S-N, BR-18618000 Botucatu, SP, Brazil
dc.identifier.doi10.1007/978-981-10-9035-6_27
dc.identifier.wosWOS:000450908300027
dc.rights.accessRightsAcesso aberto
unesp.author.lattes3468567007064752[4]
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