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Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

dc.creatorCelaya-Padilla J.
dc.creatorMartinez-Torteya A.
dc.creatorRodriguez-Rojas J.
dc.creatorGalvan-Tejada J.
dc.creatorTreviño V.
dc.creatorTamez-Peña J.
dc.date2015
dc.date.accessioned2018-04-09T17:15:20Z
dc.date.available2018-04-09T17:15:20Z
dc.identifier.issn23146133
dc.identifier.doi10.1155/2015/231656
dc.identifier.urihttp://hdl.handle.net/11285/628077
dc.descriptionMammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets. The method coregisters the left and right mammograms, extracts image features, and classifies the subjects into risk of having malignant calcifications (CS), malignant masses (MS), and healthy subject (HS). In this study, 449 subjects (197 CS, 207 MS, and 45 HS) from a public database were used to train and evaluate the CADx. Percentile-rank (p-rank) and z -normalizations were used. For the p -rank, the CS versus HS model achieved a cross-validation accuracy of 0.797 with an area under the receiver operating characteristic curve (AUC) of 0.882; the MS versus HS model obtained an accuracy of 0.772 and an AUC of 0.842. For the z -normalization, the CS versus HS model achieved an accuracy of 0.825 with an AUC of 0.882 and the MS versus HS model obtained an accuracy of 0.698 and an AUC of 0.807. The proposed method has the potential to rank cases with high probability of malignant findings aiding in the prioritization of radiologists work list. Copyright © 2015 José Celaya-Padilla et al.
dc.languageeng
dc.publisherHindawi Limited
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84937716018&doi=10.1155%2f2015%2f231656&partnerID=40&md5=6419c92a76ce386e5c043791f1d65a7e
dc.rightsopenAccess
dc.sourceBioMed Research International
dc.sourceScopus
dc.subjectArticle
dc.subjectautomation
dc.subjectbreast cancer
dc.subjectcancer risk
dc.subjectcancer screening
dc.subjectcomputer assisted diagnosis
dc.subjectcontrolled study
dc.subjectdata base
dc.subjectdiagnostic accuracy
dc.subjectdiagnostic test accuracy study
dc.subjectdigital imaging
dc.subjecthuman
dc.subjectimage enhancement
dc.subjectimage subtraction
dc.subjectmajor clinical study
dc.subjectmammography
dc.subjectmethodology
dc.subjectprobability
dc.subjectradiologist
dc.subjectsensitivity and specificity
dc.subjecttumor calcinosis
dc.subjectvalidation process
dc.subjectworkflow
dc.subjectautomated pattern recognition
dc.subjectbreast tumor
dc.subjectcomputer simulation
dc.subjectdiagnostic imaging
dc.subjectearly cancer diagnosis
dc.subjectechography
dc.subjectemergency health service
dc.subjectfemale
dc.subjectmammography
dc.subjectmiddle aged
dc.subjectmultivariate analysis
dc.subjectprocedures
dc.subjectreproducibility
dc.subjectstatistical model
dc.subjectBreast Neoplasms
dc.subjectComputer Simulation
dc.subjectEarly Detection of Cancer
dc.subjectFemale
dc.subjectHumans
dc.subjectImage Interpretation, Computer-Assisted
dc.subjectMammography
dc.subjectMiddle Aged
dc.subjectModels, Statistical
dc.subjectMultivariate Analysis
dc.subjectPattern Recognition, Automated
dc.subjectReproducibility of Results
dc.subjectSensitivity and Specificity
dc.subjectSubtraction Technique
dc.subjectTriage
dc.subjectUltrasonography
dc.titleBilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms
dc.typeArtículo
dc.identifier.volume2015
refterms.dateFOA2018-04-09T17:15:20Z


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