Multimodal genetic algorithms-based algorithm for automatic point correspondence

Delibasis, K; Asvestas, PA; Matsopoulos, GK

dc.contributor.author	Delibasis, K	en
dc.contributor.author	Asvestas, PA	en
dc.contributor.author	Matsopoulos, GK	en
dc.date.accessioned	2014-03-01T01:33:46Z
dc.date.available	2014-03-01T01:33:46Z
dc.date.issued	2010	en
dc.identifier.issn	0031-3203	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20584
dc.subject	Automatic correspondence	en
dc.subject	Elastic deformation	en
dc.subject	Image registration	en
dc.subject	Medical images	en
dc.subject	Multimodal GAs optimization	en
dc.subject	Point extraction	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Automatic correspondence	en
dc.subject.other	Automatic determination	en
dc.subject.other	Benchmark functions	en
dc.subject.other	Computer tomography	en
dc.subject.other	Deformation field	en
dc.subject.other	Free-form deformation	en
dc.subject.other	Genetic population	en
dc.subject.other	ICP algorithms	en
dc.subject.other	Iterative closest point algorithm	en
dc.subject.other	Iterative procedures	en
dc.subject.other	Local maximum	en
dc.subject.other	Local transformations	en
dc.subject.other	Medical images	en
dc.subject.other	Multi-modal	en
dc.subject.other	Multi-modal optimization	en
dc.subject.other	Multimodal function optimization	en
dc.subject.other	Non-rigid registration algorithms	en
dc.subject.other	Objective functions	en
dc.subject.other	Optimizers	en
dc.subject.other	Point correspondence	en
dc.subject.other	Point extraction	en
dc.subject.other	Point of interest	en
dc.subject.other	Quantitative criteria	en
dc.subject.other	Retinal image	en
dc.subject.other	Similarity transformation	en
dc.subject.other	Thin plate spline	en
dc.subject.other	Elastic deformation	en
dc.subject.other	Functions	en
dc.subject.other	Gases	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Image registration	en
dc.subject.other	Optimization	en
dc.subject.other	Tomography	en
dc.subject.other	Medical imaging	en
dc.title	Multimodal genetic algorithms-based algorithm for automatic point correspondence	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.patcog.2010.06.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.patcog.2010.06.009	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	In this paper, the problem of automatic determination of point correspondence between two images is formulated as a multimodal function optimization and the usefulness of genetic algorithms (GAS) as a multimodal optimizer is explored. Initially, a number of variations of GAs, capable of simultaneously discovering multiple extremes of an objective function are evaluated on a mathematical benchmark objective function with multiple unequal maxima. The variation of the GAs that performs best on the benchmark function, in terms of the number of maxima discovered, is selected for the determination of automatic point correspondence between two images. The selected variation of the GAs involves an iterative procedure for the formation of a genetic population of individuals (or chromosomes). Each individual encodes the position of a point of interest on one of the available images as well as parameters of a local transformation that generates the position of the corresponding point on the other image. The proposed algorithm aims to discover individuals that corresponds to local maxima of an objective function that measures the similarity between patches of the two images. When the GAs-based multimodal optimization algorithm terminates, pairs of corresponding points between the two images are obtained that can be used for the generation of a dense deformation field by means of the thin plate splines model. The proposed algorithm is applied to 2D medical images (dental and retinal images) under known transformations (similarity and elastic transformation) and is also assessed on medical images with unknown transformations (computer tomography transverse slices). The proposed algorithm is compared against the iterative closest point (ICP) algorithm, and a well-known non-rigid registration algorithm, based on free-form deformations (FFD) using various quantitative criteria. The obtained results indicate that in case of known similarity transformations, the proposed multimodal GAs-based algorithm and the ICP algorithm present equivalent performance, whereas the FFD algorithm is clearly outperformed. In the case of known sinousoidal deformations, the proposed multimodal GAs-based and the FFD algorithm achieve equivalent performance and clearly outperform the ICP algorithm. Finally, in the case of unknown elastic deformations, the proposed GAs-based algorithm appears to perform marginally better than the FFD algorithm, whereas it clearly outperforms the ICP algorithm. (C) 2010 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Pattern Recognition	en
dc.identifier.doi	10.1016/j.patcog.2010.06.009	en
dc.identifier.isi	ISI:000282384900009	en
dc.identifier.volume	43	en
dc.identifier.issue	12	en
dc.identifier.spage	4011	en
dc.identifier.epage	4027	en