Application of the Kohonen network for automatic point correspondence in retinal images

dc.contributor.author	Μαρκάκη, Βασιλική Ε.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Ουζούνογλου, Νικόλαος Κ.	el
dc.date.accessioned	2015-01-13T13:59:22Z
dc.date.issued	2015-01-13
dc.identifier.uri	http://hdl.handle.net/11400/3932
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.subject	Eye--Muscles--Abnormalities
dc.subject	Image registration
dc.subject	Μάτι
dc.subject	Εγγραφή εικόνας
dc.title	Application of the Kohonen network for automatic point correspondence in retinal images	en
heal.type	conferenceItem
heal.generalDescription	Proceedings	el
heal.classification	Medicine
heal.classification	Ophthalmology
heal.classification	Ιατρική
heal.classification	Οφθαλμολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85095071
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Οφθαλμολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2007005799
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2007005742
heal.identifier.secondary	DOI: 10.1109/IEMBS.2007.4353840
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	2007
heal.bibliographicCitation	Markaki, V., Asvestas, P., Matsopoulos, G. and Uzunoglou, N. (2007). Application of the Kohonen network for automatic point correspondence in retinal images. In the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, 22th-26th August 2007. pp. 6467 - 6470. IEEE.	en
heal.abstract	In this paper, an algorithm for automatic point correspondence is proposed towards retinal image registration. Given a pair of corresponding retinal images and a set of bifurcations or other salient points in one of the images, the algorithm detects effectively the set of corresponding points in the second image, by exploiting the properties of Kohonen's Self Organizing Maps and embedding them in a stochastic optimization procedure. The proposed algorithm was tested on 20 unimodal retinal pairs and the obtained results show an enhanced performance in terms of accuracy and robustness compared to the existing algorithm, on which it is based.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	Annual International Conference of the IEEE Engineering in Medicine and Biology Society	en
heal.conferenceItemType	full paper