Automatic retinal image registration scheme using global optimization techniques

Matsopoulos, GK; Mouravliansky, NA; Delibasis, KK; Nikita, KS

dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Mouravliansky, NA	en
dc.contributor.author	Delibasis, KK	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T01:14:26Z
dc.date.available	2014-03-01T01:14:26Z
dc.date.issued	1999	en
dc.identifier.issn	10897771	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13061
dc.subject	Genetic algorithms	en
dc.subject	Image registration	en
dc.subject	Retinal images	en
dc.subject	Simulated annealing	en
dc.subject	Transformation models	en
dc.subject.other	algorithm	en
dc.subject.other	angiography	en
dc.subject.other	article	en
dc.subject.other	human	en
dc.subject.other	image processing	en
dc.subject.other	retina blood vessel	en
dc.subject.other	Algorithms	en
dc.subject.other	Angiography	en
dc.subject.other	Humans	en
dc.subject.other	Image Processing, Computer-Assisted	en
dc.subject.other	Retinal Vessels	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Mathematical transformations	en
dc.subject.other	Simulated annealing	en
dc.subject.other	Chorioangiography	en
dc.subject.other	Fluorescein angiography	en
dc.subject.other	Image registration	en
dc.subject.other	Angiography	en
dc.title	Automatic retinal image registration scheme using global optimization techniques	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/4233.748975	en
heal.identifier.secondary	http://dx.doi.org/10.1109/4233.748975	en
heal.publicationDate	1999	en
heal.abstract	Retinal image registration is commonly required in order to combine the complementary information in different retinal modalities. In this paper, a new automatic scheme to register retinal images is presented and is currently tested in a clinical environment. The scheme considers the suitability and efficiency of different image transformation models and function optimization techniques, following an initial preprocessing stage. Three different transformation models-affine, bilinear and projective-as well as three optimization techniques-downhill simplex method, simulated annealing and genetic algorithms-are investigated and compared in terms of accuracy and efficiency. The registration of 26 pairs of Fluoroscein Angiography and Indocyanine Green Chorioangiography images with the corresponding Red-Free retinal images, showed the superiority of combining genetic algorithms with the affine and bilinear transformation models. A comparative study of the proposed automatic registration scheme against the manual method, commonly used in the clinical practice, is finally presented showing the advantage of the proposed automatic scheme in terms of accuracy and consistency. © 1999 IEEE.	en
heal.journalName	IEEE Transactions on Information Technology in Biomedicine	en
dc.identifier.doi	10.1109/4233.748975	en
dc.identifier.volume	3	en
dc.identifier.issue	1	en
dc.identifier.spage	47	en
dc.identifier.epage	60	en