Improving microarray spots segmentation by k-means driven adaptive image restoration

dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Μπουγιούκος, Παναγιώτης	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Αργυρόπουλος, Χρήστος	el
dc.date.accessioned	2015-05-14T16:08:43Z
dc.date.available	2015-05-14T16:08:43Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10386
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://medlab.cc.uoi.gr/itab2006/proceedings/Computational%20Biology%20and%20Bioinformatics/98.pdf	en
dc.subject	Microarray
dc.subject	Gene
dc.subject	Μικροσυστοιχίες
dc.subject	Γονίδιο
dc.title	Improving microarray spots segmentation by k-means driven adaptive image restoration	en
heal.type	conferenceItem
heal.generalDescription	Proceedings	en
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	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	Daskalakis, A., Cavouras, D., Bougioukos, P., Kostopoulos, S., Argyropoulos, C., et al. (2006). Improving microarray spots segmentation by k-means driven adaptive image restoration. Proceedings of the International Special Topic Conference on Information Technology in Biomedicine. Ioannina, Greece, 26th-28th October 2006. Available from: http://medlab.cc.uoi.gr/itab2006/proceedings/Computational%20Biology%20and%20Bioinformatics/98.pdf	en
heal.abstract	Complementary DNA microarray experiments are used to study human genome. However, microarray images are corrupted by spatially inhomogeneous noise that deteriorates image and consequently gene expression. An adaptive microarray image restoration technique is developed by suitably combining unsupervised clustering with the restoration filters for boosting the performance of microarray spots segmentation and for improving the accuracy of subsequent gene expression. Microarray images comprised a publicly available dataset of seven images, obtained from the database of the MicroArray Genome Imaging & Clustering Tool website. Each image contained 6400 spots investigating the diauxic shift of Saccharomyces cerevisiae. The adaptive microarray image restoration technique combined 1/a griding algorithm for locating individual cell images, 2/a clustering algorithm, for assessing local noise from the spot’s background, and 3/a wiener restoration filter, for enhancing individual spots. The effect of the proposed technique quantified using a well-known boundary detection algorithm (Gradient Vector Flow snake) and the information theoretic metric of Jeffrey’s divergence. The proposed technique increased the Jeffrey’s metric from 0.0194 bits to 0.0314 bits, while boosted the performance of the employed boundary detection algorithm. Application of the proposed technique on cDNA microarray images resulted in noise suppression and facilitated spot edge detection.	en
heal.fullTextAvailability	true
heal.conferenceName	International Special Topic Conference on Information Technology in Biomedicine	en
heal.conferenceItemType	poster