Artificial immune network for automatic point correspondence in medical images

dc.contributor.author	Δελήμπασης, Κωνσταντίνος Κ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Μουραβλιάνσκυ, Νικόλαος Α.	el
dc.contributor.author	Οικονομόπουλος, Θεόδωρος Λ.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-08T10:06:18Z
dc.date.available	2015-02-08T10:06:18Z
dc.date.issued	2015-02-08
dc.identifier.uri	http://hdl.handle.net/11400/5837
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://ieeexplore.ieee.org/	en
dc.subject	Artificial immune systems
dc.subject	Deformation
dc.subject	Τεχνητό ανοσοποιητικό σύστημα
dc.subject	Παραμόρφωση
dc.title	Artificial immune network for automatic point correspondence in medical images	en
heal.type	conferenceItem
heal.generalDescription	Conference proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society	en
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: 10.1109/IEMBS.2007.4352421
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2007
heal.bibliographicCitation	Delibasis, K., Asvestas, P., Mouravliansky, N., Economopoulos, T. and Matsopoulos, G. (2007). Artificial immune network for automatic point correspondence in medical images. In the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, 22nd -26th Aug 2007. pp. 840-843. IEEE. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	In this work, an automatic method for point-by point correspondence between medical images is presented based on the implementation of an Artificial Immune Network (AIN). AIN is a relatively novel population based algorithm, which when applied to multimodal function optimization exhibit the attractive feature of locating, the global minimum of a function, as well as a large number of strong local optimum points. In this work, AIN has been modified and applied to the problem of automatic point correspondence from pairs of images. Additionally, the proposed system is capable of altering the initially selected points on the reference image so that the population of points becomes fitter. The performance of the proposed algorithm using the AIN is evaluated against a standardized method for automatic correspondence, the template matching, in terms of the accuracy of the correspondence. Qualitative and quantitative results presented from in vitro radiographic dental images with synthetic deformations, show that the proposed algorithm outperforms the template matching for automatic point correspondence.	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