Unsupervised SVM-based gridding for DNA microarray images

dc.contributor.author	Μπαριάμης, Δημήτριος	el
dc.contributor.author	Μαρούλης, Δημήτρης Ε.	el
dc.contributor.author	Ιακωβίδης, Δημήτρης Κ.	el
dc.date.accessioned	2015-06-06T22:35:57Z
dc.date.available	2015-06-06T22:35:57Z
dc.date.issued	2015-06-07
dc.identifier.uri	http://hdl.handle.net/11400/15371
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.sciencedirect.com/science/article/pii/S0895611109001165	en
dc.subject	Gridding
dc.subject	Rotation estimation
dc.subject	Πλέγμα
dc.subject	Υπολογισμός περιστροφής
dc.title	Unsupervised SVM-based gridding for DNA microarray images	en
heal.type	journalArticle
heal.generalDescription	Biomedical Image Technologies and Methods - BIBE 2008	en
heal.classification	Technology
heal.classification	Electronics
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρονική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://zbw.eu/stw/descriptor/10455-2
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρονική
heal.identifier.secondary	doi:10.1016/j.compmedimag.2009.09.005
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε.	el
heal.publicationDate	2010-09
heal.bibliographicCitation	Bariamis, D., Maroulis, D. and Iakovidis, D. (September 2010). Unsupervised SVM-based gridding for DNA microarray images. Computerized Medical Imaging and Graphics. 34(6). pp. 418-425. Elsevier B.V: 2010. Available from: http://www.sciencedirect.com/science/article/pii/S0895611109001165 [Accessed 29/10/2009]	en
heal.abstract	This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Computerized Medical Imaging and Graphics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true