Application of Kohonen network for automatic point correspondence in 2D medical images

dc.contributor.author	Μαρκάκη, Βασιλική Ε.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-09T09:24:24Z
dc.date.available	2015-02-09T09:24:24Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5902
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.elsevier.com	en
dc.subject	Automatic correspondence
dc.subject	Medical images
dc.subject	Αυτόματη αλληλογραφία
dc.subject	Ιατρικές εικόνες
dc.title	Application of Kohonen network for automatic point correspondence in 2D medical images	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.identifier.secondary	doi:10.1016/j.compbiomed.2009.04.006
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2009
heal.bibliographicCitation	Markaki, V., Asvestas, P. and Matsopoulos, G. (July 2009). Application of Kohonen network for automatic point correspondence in 2D medical images. Computers in Biology and Medicine. 39(7). pp. 630-645. Elsevier Science Ltd. Available from: http://www.sciencedirect.com [Accessed 29/05/2009]	en
heal.abstract	In this paper, a generalized application of Kohonen Network for automatic point correspondence of unimodal medical images is presented. Given a pair of two-dimensional medical images of the same anatomical region and a set of interest points in one of the images, the algorithm detects effectively the set of corresponding points in the second image, by exploiting the properties of the Kohonen self organizing maps (SOMs) and embedding them in a stochastic optimization framework. The correspondences are established by determining the parameters of local transformations that map the interest points of the first image to their corresponding points in the second image. The parameters of each transformation are computed in an iterative way, using a modification of the competitive learning, as implemented by SOMs. The proposed algorithm was tested on medical imaging data from three different modalities (CT, MR and red-free retinal images) subject to known and unknown transformations. The quantitative results in all cases exhibited sub-pixel accuracy. The algorithm also proved to work efficiently in the case of noise corrupted data. Finally, in comparison to a previously published algorithm that was also based on SOMs, as well as two widely used techniques for detection of point correspondences (template matching and iterative closest point), the proposed algorithm exhibits an improved performance in terms of accuracy and robustness.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Computers in Biology and Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true