A wavelet - based markov random field segmentation model in segmenting microarray experiments

dc.contributor.author	Αθανασιάδης, Εμμανουήλ Ι.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-14T09:54:34Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10353
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://www.cmpbjournal.com/article/S0169-2607(11)00072-1/abstract	en
dc.source	http://www.sciencedirect.com/science/article/pii/S0169260711000721	en
dc.subject	Image segmentation
dc.subject	Wavelets (Mathematics)
dc.subject	Κατάτμηση εικόνας
dc.subject	Κυμάτιο
dc.title	A wavelet - based markov random field segmentation model in segmenting microarray experiments	en
heal.type	journalArticle
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.keywordURI	http://id.loc.gov/authorities/subjects/sh91006163
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.identifier.secondary	DOI: http://dx.doi.org/10.1016/j.cmpb.2011.03.007
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	2011
heal.bibliographicCitation	Athanasiadis, E., Cavouras, D., Kostopoulos, S., Glotsos, D., Kalatzis, I., et al. (December 2011). A wavelet - based markov random field segmentation model in segmenting microarray experiments. Computer Methods and Programs in Biomedicine. 104(3). pp. 307-315. Elsevier Ireland Ltd: 2011.	en
heal.abstract	In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r2), and the concordance correlation (pc) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r2, and pc (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.	en
heal.publisher	Elsevier Ireland Ltd	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false