Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/10379
Title: Wiener-based deconvolution methods for improving the accuracy of spot segmentation in microarray images
Authors: Δασκαλάκης, Αντώνης
Αργυρόπουλος, Χρήστος
Γκλώτσος, Δημήτριος
Κωστόπουλος, Σπυρίδων
Αθανασιάδης, Εμμανουήλ Ι.
Contributors: Κάβουρας, Διονύσης Α.
Νικηφορίδης, Γεώργιος Σ.
Item type: Conference publication
Conference Item Type: Full Paper
Keywords: Microarray;Gene;Μικροσυστοιχίες;Γονίδιο
Subjects: Technology
Biomedical engineering
Τεχνολογία
Βιοϊατρική τεχνολογία
Issue Date: 14-May-2015
2006
Date of availability: 14-May-2015
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.
Language: English
Citation: 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
Conference: European Symposium on Biomedical Engineering
Access scheme: Publicly accessible
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/10379
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