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Multimodal genetic algorithms-based algorithm for automatic point correspondence

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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 http://hdl.handle.net/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


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