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

Delibasis, KK; Asvestas, PA; Matsopoulos, GK

dc.contributor.author	Delibasis, KK	en
dc.contributor.author	Asvestas, PA	en
dc.contributor.author	Matsopoulos, GK	en
dc.date.accessioned	2014-03-01T01:35:21Z
dc.date.available	2014-03-01T01:35:21Z
dc.date.issued	2011	en
dc.identifier.issn	0895-6111	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21010
dc.subject	Artificial immune system	en
dc.subject	Iterative Closest Point	en
dc.subject	Medical image registration	en
dc.subject	Mutual Information	en
dc.subject	Point correspondence	en
dc.subject	Point extraction	en
dc.subject.classification	Radiology, Nuclear Medicine & Medical Imaging	en
dc.subject.other	Artificial Immune System	en
dc.subject.other	Iterative Closest Points	en
dc.subject.other	Medical image registration	en
dc.subject.other	Mutual informations	en
dc.subject.other	Point correspondence	en
dc.subject.other	Point extraction	en
dc.subject.other	Algorithms	en
dc.subject.other	Image registration	en
dc.subject.other	Immunology	en
dc.subject.other	Optimization	en
dc.subject.other	Medical imaging	en
dc.subject.other	accuracy	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	artificial immune system	en
dc.subject.other	correspondence analysis	en
dc.subject.other	imaging	en
dc.subject.other	methodology	en
dc.subject.other	performance	en
dc.subject.other	priority journal	en
dc.subject.other	registration	en
dc.subject.other	retina image	en
dc.subject.other	tooth radiography	en
dc.subject.other	X ray	en
dc.subject.other	Algorithms	en
dc.subject.other	Humans	en
dc.subject.other	Image Enhancement	en
dc.subject.other	Radiographic Image Interpretation, Computer-Assisted	en
dc.subject.other	Tooth	en
dc.title	Automatic point correspondence using an artificial immune system optimization technique for medical image registration	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.compmedimag.2010.09.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.compmedimag.2010.09.002	en
heal.language	English	en
heal.publicationDate	2011	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. (C) 2010 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computerized Medical Imaging and Graphics	en
dc.identifier.doi	10.1016/j.compmedimag.2010.09.002	en
dc.identifier.isi	ISI:000287561700004	en
dc.identifier.volume	35	en
dc.identifier.issue	1	en
dc.identifier.spage	31	en
dc.identifier.epage	41	en