Complementary DNA microarray image processing based on the fuzzy gaussian mixture model

dc.contributor.author	Αθανασιάδης, Εμμανουήλ Ι.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-13T11:24:26Z
dc.date.issued	2015-05-13
dc.identifier.uri	http://hdl.handle.net/11400/10286
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://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5156198&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5156198	en
dc.subject	Bioinformatics
dc.subject	Molecular biophysics
dc.subject	Βιοπληροφορική
dc.subject	Μοριακή βιοφυσική
dc.title	Complementary DNA microarray image processing based on the fuzzy gaussian mixture model	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh00003585
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.identifier.secondary	DOI: 10.1109/TITB.2008.907984
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2009
heal.bibliographicCitation	Athanasiadis, E., Cavouras, D., Spyridonos, P., Glotsos, D., Kalatzis, I., et al. (July 2009). Complementary DNA microarray image processing based on the fuzzy gaussian mixture model. IEEE Transaction on Information Technology in Biomedicine. 13(4). pp. 419-425. IEEE: 2009.	en
heal.abstract	The objective of this paper was to investigate the segmentation ability of the fuzzy Gaussian mixture model (FGMM) clustering algorithm, applied on complementary DNA (cDNA) images. Following a standard established procedure, a simulated microarray image of 1600 cells, each containing one spot, was produced. For further evaluation of the algorithm, three real microarray images were also used, each containing 6400 spots. For the task of locating spot borders and surrounding background (BG) in each cell, an automatic gridding process was developed and applied on microarray images. The FGMM and the Gaussian mixture model (GMM) algorithms were applied to each cell with the purpose of discriminating foreground (FG) from BG. The segmentation abilities of both algorithms were evaluated by means of the segmentation matching factor, coefficient of determination, and concordance correlation, in respect to the actual classes (FG-BG pixels) of the simulated spots. Pairwise correlation and mean absolute error of the real images among replicates were also calculated. The FGMM was found to perform better and with equal processing time, as compared to the GMM, rendering the FGMM algorithm an efficient alternative for segmenting cDNA microarray images.	en
heal.publisher	IEEE	en
heal.journalName	IEEE Transaction on Information Technology in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true