Wiener-based deconvolution methods for improving the accuracy of spot segmentation in microarray images

dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Αργυρόπουλος, Χρήστος	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Αθανασιάδης, Εμμανουήλ Ι.	el
dc.date.accessioned	2015-05-14T15:47:21Z
dc.date.available	2015-05-14T15:47:21Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10379
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/Daskalakis_full%20paper.pdf	en
dc.subject	Microarray
dc.subject	Gene
dc.subject	Μικροσυστοιχίες
dc.subject	Γονίδιο
dc.title	Wiener-based deconvolution methods for improving the accuracy of spot segmentation in 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	Daskalakis, A., Argyropoulos, C., Glotsos, D., Kostopoulos, S., Athanasiadis, E., et al. (2006). Wiener-based deconvolution methods for improving the accuracy of spot segmentation in microarray images. In the 5th European Symposium on Biomedical Engineering. Patras, Greece, 7th-9th July 2006. Available from: http://bme.med.upatras.gr/ESBME2006/CD/5th_ESBME_2006_PDFs/Session_5/Daskalakis_full%20paper.pdf	en
heal.abstract	Purpose: Microarray experiments are important tools for high throughput gene quantification. Nevertheless, such experiments are confounded by a number of technical factors, which operate at the fabrication, target labelling, and hybridization stages, and result in spatially inhomogeneous noise. Unless these sources of error are addressed, they will propagate throughout the stages of the analysis, leading to inaccurate biological inferences. The aim of this study was to investigate whether image restoration techniques may improve the accuracy of subsequent microarray image analysis steps (i.e. segmentation and gene quantification). Materials and Methods: A public dataset of seven microarrays obtained from the MicroArray Genome Imaging & Clustering Tool (MAGIC) database were used. Each image contained 6400 spots investigating the diauxic shift of Saccharomyces cerevisiae. Restoration was based on the Wiener deconvolution. Subsequently, restored images were processed with the MAGIC tool for semi-automatic griding and segmentation. The influence of the restoration process on the accuracy of spot segmentation was quantitatively assessed by the information theoretic metric of the Kullback-Liebler divergence. Results: Pre-processing based on Wiener deconvolution increased the range of divergence (0.04 – 3.01 bits) and consequently improved the accuracy of subsequent spot segmentation. Conclusion: Information theoretic metrics confirmed the importance of image restoration as a preprocessing step that significantly improved the accuracy of subsequent segmentation, thus leading to more accurate gene quantification.	en
heal.fullTextAvailability	true
heal.conferenceName	European Symposium on Biomedical Engineering	en
heal.conferenceItemType	full paper