Comparative evaluation of a gaussian mixture models and a seeded region growing techniques for the segmentation of microarray images

dc.contributor.author	Αθανασιάδης, Εμμανουήλ Ι.	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-14T16:00:35Z
dc.date.available	2015-05-14T16:00:35Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10382
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://bme.med.upatras.gr/ESBME2006/CD/5th_ESBME_2006_PDFs/Session_5/Athanasiadis_full%20paper.pdf	en
dc.subject	Microarray image
dc.subject	Segmentation
dc.subject	Εικόνες μικροσυστοιχιών
dc.subject	Τμηματοποίηση
dc.title	Comparative evaluation of a gaussian mixture models and a seeded region growing techniques for the segmentation of microarray images	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Biomedical engineering
heal.classification	Τεχνολογία
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.language	en
heal.access	free
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2006
heal.bibliographicCitation	Athanasiadis, E., Daskalakis, A., Spyridonos, P., Glotsos, D., Kalatzis, I., et al. (2006). Comparative evaluation of a gaussian mixture models and a seeded region growing techniques for the segmentation of microarray images. In the 5th European Symposium on Biomedical Engineering. University of Patras, Cultural and Conference Center Patras. Patras, Greece, 7th – 9th July 2006. Available from: http://bme.med.upatras.gr/ESBME2006/CD/5th_ESBME_2006_PDFs/Session_5/Athanasiadis_full%20paper.pdf	en
heal.abstract	The purpose of the present study was to investigate and compare the segmentation ability of the Gaussian Mixture Models (GMM) against the Seeded Region Growing (SRG) methods in microarray spots segmentation. A simulated microarray image, each containing 200 spots, was produced. An automatic gridding process was developed in MATLAB and it was applied on the images for identifying the centers of spots and their surrounding borders (cells). The GMM, developed in MATLAB and the SRG algorithms, using MAGIC Tool software, were applied to each spot separately for discriminating foreground from background. The segmentation abilities of the GMM and SRG algorithms were evaluated by calculating the segmentation matching factor for each spot. Optimal segmentation results were obtained by the GMM, especially in cases where the spot’s mean intensity value was close to the background. The GMM technique was found to be an accurate algorithm in delineating the boundary of microarray spots and, thus, in discriminating the spot from its surrounding background.	en
heal.fullTextAvailability	true
heal.conferenceName	European Symposium on Biomedical Engineering	en
heal.conferenceItemType	full paper