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dc.contributor.authorBlekas, K.en
dc.contributor.authorGalatsanos, N. P.en
dc.contributor.authorLikas, A.en
dc.contributor.authorLagaris, I. E.en
dc.rightsDefault Licence-
dc.subjectcross-validated likelihooden
dc.subjectDNA microarray image analysisen
dc.subjectexpectation-maximization algorithmen
dc.subjectgaussian mixture modelsen
dc.subjectmarkov random fieldsen
dc.subjectmaximum a posteriorien
dc.subjectmaximum likelihooden
dc.subjectmicroarray griddingen
dc.subjectem algorithmen
dc.titleMixture model analysis of DNA microarray imagesen
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDoi 10.1109/Tmi.2005.848358-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.abstractIn this paper, we propose a new methodology for analysis of microarray images. First, a new gridding algorithm is proposed for determining the individual spots and their borders. Then, a Gaussian mixture model (GMM) approach is presented for the analysis of the individual spot images. The main advantages of the proposed methodology are modeling flexibility and adaptability to the data, which are well-known strengths of GMM. The maximum likelihood and maximum a posteriori approaches are used to estimate the GMM parameters via the expectation maximization algorithm. The proposed approach has the ability to detect and compensate for artifacts that might occur in microarray images. This is accomplished by a model-based criterion that selects the number of the mixture components. We present numerical experiments with artificial and real data where we compare the proposed approach with previous ones and existing software tools for microarray image analysis and demonstrate its advantages.en
heal.journalNameIEEE Trans Med Imagingen
heal.journalTypepeer reviewed-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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