Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/10403
Full metadata record
DC FieldValueLanguage
dc.contributor.authorΑθανασιάδης, Εμμανουήλ Ι.ell
dc.contributor.authorΚάβουρας, Διονύσης Α.ell
dc.contributor.authorΣπυρίδωνος, Παναγιώτα Π.ell
dc.contributor.authorΓκλώτσος, Δημήτριοςell
dc.contributor.authorΚαλατζής, Ιωάννηςell
dc.date.accessioned2015-05-14T17:11:40Z-
dc.date.available2015-05-14T17:11:40Z-
dc.date.issued2015-05-14-
dc.date.issued2007-
dc.identifier.citationAthanasiadis, E., Cavouras, D., Spyridonos, P., Glotsos, D., Kalatzis, I., et al. (2007). Segmentation of microarray images using gradient vector flow active contours boosted by gaussian mixture models. In the 2nd International Conference on Experiments/ Process/ System Modelling/ Simulation/ Optimization (2nd IC-EpsMsO). Athens, Greece, 4th – 7th July 2007. Available from: http://www.bme.teiath.gr/medisp/pdfs/ATHANASSIADIS_2007_EpsMso_Segmentation%20of%20microarray.pdfeng
dc.identifier.urihttp://hdl.handle.net/11400/10403-
dc.description.abstractIn this paper, a new methodology for the segmentation of cDNA microarray images is proposed, based on the combination of Gaussian Mixture Models (GMM) with Gradient Vector Flow (GVF) active contours. A simulated microarray image of 1000 spots was produced using a standard procedure. 5 real microarray images were used to evaluate the performance of our algorithm. GMM was firstly applied in all individual cells (spot with each background). The output was used to initialize a GVF active contour. The major advance of our method is that it overcomes limitations of both GMM and active contours when used individually. Segmentation matching factors and mean intensity values were calculated for every cell using GMM, GVF, and the combination of GMM and GVF in the simulated data. Pairwise correlations and mean absolute errors were also calculated by using real microarrays. Numerical experiments using both simulated and real images showed that our method was more accurate in measuring intensity values and detecting actual boundaries of spots, compared with GMM and active contours used individually. Results concerning the segmentability and the mean intensity value of the proposed algorithm were more accurate, as compared with those methods when used individually.eng
dc.language.isoeng-
dc.rightsΑναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.sourcehttp://www.bme.teiath.gr/medisp/pdfs/ATHANASSIADIS_2007_EpsMso_Segmentation%20of%20microarray.pdfen
dc.subjectMicroarrays-
dc.subjectActive Contours-
dc.subjectΜικροσυστοιχίες-
dc.subjectΕνεργά περιγράμματα-
dc.titleSegmentation of microarray images using gradient vector flow active contours boosted by gaussian mixture modelseng
dc.typeΔημοσίευση σε συνέδριο-
heal.classificationMedicine-
heal.classificationBiomedical engineering-
heal.classificationΙατρική-
heal.classificationΒιοϊατρική τεχνολογία-
heal.classificationURIhttp://id.loc.gov/authorities/subjects/sh00006614-
heal.classificationURIhttp://id.loc.gov/authorities/subjects/sh85014237-
heal.classificationURI**N/A**-Ιατρική-
heal.classificationURI**N/A**-Βιοϊατρική τεχνολογία-
heal.contributorNameΝικηφορίδης, Γεώργιος Σ.ell
heal.accessfree-
heal.recordProviderΤ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.ell
heal.fullTextAvailabilitytrue-
heal.conferenceNameInternational Conference on Experiments/ Process/ System Modelling/ Simulation/ Optimizationeng
heal.conferenceItemTypeposter-
Appears in Collections:Δημοσιεύσεις



This item is licensed under a Creative Commons License Creative Commons