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dc.contributor.authorPapadopoulos, A.en
dc.contributor.authorFotiadis, D. I.en
dc.contributor.authorCostaridou, L.en
dc.date.accessioned2015-11-24T17:34:50Z-
dc.date.available2015-11-24T17:34:50Z-
dc.identifier.issn0010-4825-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/14039-
dc.rightsDefault Licence-
dc.subjectmammography image enhancementen
dc.subjectpreprocessingen
dc.subjectmicrocalcification detectionen
dc.subjectcaden
dc.subjectcomputer-aided diagnosisen
dc.subjectcontrast enhancementen
dc.subjectdigital mammographyen
dc.subjectfuzzy-logicen
dc.subjecthistogram equalizationen
dc.subjectprocessing techniquesen
dc.subjectneural-networken
dc.subjectalgorithmsen
dc.subjectwaveletsen
dc.subjectmodelsen
dc.titleImprovement of microcalcification cluster detection in mammography utilizing image enhancement techniquesen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDOI 10.1016/j.compbiomed.2008.07.006-
heal.identifier.secondary<Go to ISI>://000260637700001-
heal.identifier.secondaryhttp://ac.els-cdn.com/S0010482508001042/1-s2.0-S0010482508001042-main.pdf?_tid=0657eccfc1be77ca67584c9bb9a755c3&acdnat=1339758312_5e92632d1e60f0dee75f285e099ccca5-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικώνel
heal.publicationDate2008-
heal.abstractIn this work. the effect of an image enhancement processing stage and the parameter tuning of a computer-aided detection (CAD) system for the detection of microcalcifications in mammograms is assessed. Five (5) image enhancement algorithms were tested introducing the contrast-limited adaptive histogram equalization (CLAHE), the local range modification (LRM) and the redundant discrete wavelet (RDW) linear stretching and shrinkage algorithms. CAD tuning optimization was targeted to the percentage of the most contrasted pixels and the size of the minimum detectable object which could satisfactorily represent a microcalcification. The highest performance in two mammographic datasets, were achieved for LRM (A(Z) = 0.932) and the wavelet-based linear stretching (AZ = 0.926) methodology. (C) 2008 Elsevier Ltd. All rights reserved.en
heal.publisherElsevieren
heal.journalNameComput Biol Meden
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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