Automatic point correspondence using an artificial immune system optimization technique for medical image registration

dc.contributor.author	Δελήμπασης, Κωνσταντίνος Κ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-09T10:59:55Z
dc.date.available	2015-02-09T10:59:55Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5916
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	Medical image registration
dc.subject	Artificial immune system
dc.subject	Ιατρική εικόνα
dc.subject	Τεχνητό ανοσοποιητικό σύστημα
dc.title	Automatic point correspondence using an artificial immune system optimization technique for medical image registration	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.compmedimag.2010.09.002
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2011
heal.bibliographicCitation	Delibasis, K., Asvestas, P. and Matsopoulos, G. (January 2011). Automatic point correspondence using an artificial immune system optimization technique for medical image registration. Computerized Medical Imaging and Graphics. 35(1). pp. 31-41. Elsevier Science Ltd: 2011. Available from: http://www.sciencedirect.com [Accessed 02/10/2010]	en
heal.abstract	In this paper, an automatic method for determining pairs of corresponding points between medical images is proposed. The method is based on the implementation of an artificial immune system (AIS). AIS is a relatively novel, population based category of algorithms, inspired by theoretical immunologic models. When used as function optimizers, AIS have the attractive property of locating the global optimum of a function as well as a large number of strong local optimum points. In this work, AIS has been applied both for the extraction of an optimal set of candidate points on the reference image and the definition of their corresponding ones on the second image. The performance of the proposed AIS algorithm is evaluated against the widely used Iterative Closest Point (ICP) algorithm in terms of the accuracy of the obtained correspondences and in terms of the accuracy of the point-based registration by the two correspondence algorithms and the Mutual Information criterion, as an intensity-based registration method. Qualitative and quantitative results involving 92 X-ray dental and 10 retinal image pairs subject to known and unknown transformations are presented. The results indicate a superior performance of the proposed AIS algorithm with respect to the ICP algorithm and the Mutual Information, in terms of both correct correspondence and registration accuracy.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Computerized Medical Imaging and Graphics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true