Segmentation of complementary DNA microarray images by wavelet-based markov random field model

dc.contributor.author	Αθανασιάδης, Εμμανουήλ Ι.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Γεωργιάδης, Παντελής	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-13T11:35:05Z
dc.date.issued	2015-05-13
dc.identifier.uri	http://hdl.handle.net/11400/10287
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	Segmentation of complementary DNA microarray images by wavelet-based markov random field model	en
dc.subject	Algorithms
dc.subject	Wavelets (Mathematics)
dc.subject	Αλγόριθμοι
dc.subject	Κυμάτιο
dc.title	Segmentation of complementary DNA microarray images by wavelet-based markov random field model	en
heal.type	journalArticle
heal.generalDescription	art. no. 5268205	en
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/sh91006163
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.identifier.secondary	DOI: 10.1109/TITB.2009.2032332
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., Glotsos, D., Georgiadis, P., Kalatzis, I., et al. (September 2009). Segmentation of complementary DNA microarray images by wavelet-based markov random field model. IEEE Transactions on Information Technology in Biomedicine. 13(6). pp. 1068-1074. IEEE: 2009	en
heal.abstract	A wavelet-based modification of the Markov random field (WMRF) model is proposed for segmenting complementary DNA (cDNA) microarray images. For evaluation purposes, five simulated and a set of five real microarray images were used. The one-level stationary wavelet transform (SWT) of each microarray image was used to form two images, a denoised image, using hard thresholding filter, and a magnitude image, from the amplitudes of the horizontal and vertical components of SWT. Elements from these two images were suitably combined to form the WMRF model for segmenting spots from their background. The WMRF was compared against the conventional MRF and the Fuzzy C means (FCM) algorithms on simulated and real microarray images and their performances were evaluated by means of the segmentation matching factor (SMF) and the coefficient of determination (r 2). Additionally, the WMRF was compared against the SPOT and SCANALYZE, and performances were evaluated by the mean absolute error (MAE) and the coefficient of variation (CV). The WMRF performed more accurately than the MRF and FCM (SMF: 92.66, 92.15, and 89.22, r 2 : 0.92, 0.90, and 0.84, respectively) and achieved higher reproducibility than the MRF, SPOT, and SCANALYZE (MAE: 497, 1215, 1180, and 503, CV: 0.88, 1.15, 0.93, and 0.90, respectively).	en
heal.publisher	IEEE	en
heal.journalName	IEEE Transactions on Information Technology in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true