An iterative point correspondence algorithm for automatic image registration: An application to dental subtraction radiography

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dc.contributor.author Markaki, VE en
dc.contributor.author Asvestas, PA en
dc.contributor.author Matsopoulos, GK en
dc.date.accessioned 2014-03-01T01:29:50Z
dc.date.available 2014-03-01T01:29:50Z
dc.date.issued 2009 en
dc.identifier.issn 0169-2607 en
dc.identifier.uri http://hdl.handle.net/123456789/19368
dc.subject Automatic point correspondence en
dc.subject Dental subtraction radiography en
dc.subject Iterative Closest Point en
dc.subject Mutual information en
dc.subject Point-based registration en
dc.subject Template matching en
dc.subject.classification Computer Science, Interdisciplinary Applications en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.classification Engineering, Biomedical en
dc.subject.classification Medical Informatics en
dc.subject.other Automatic point correspondence en
dc.subject.other Dental subtraction radiography en
dc.subject.other Iterative Closest Point en
dc.subject.other Mutual information en
dc.subject.other Point-based registration en
dc.subject.other Image enhancement en
dc.subject.other Image registration en
dc.subject.other Radiography en
dc.subject.other Radiology en
dc.subject.other Template matching en
dc.subject.other Image processing en
dc.subject.other Algorithms en
dc.subject.other Biometry en
dc.subject.other Dental Models en
dc.subject.other Humans en
dc.subject.other Radiographic Image Interpretation, Computer-Assisted en
dc.subject.other Radiography, Dental, Digital en
dc.title An iterative point correspondence algorithm for automatic image registration: An application to dental subtraction radiography en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.cmpb.2008.07.003 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.cmpb.2008.07.003 en
heal.language English en
heal.publicationDate 2009 en
heal.abstract In this paper, an Automatic Iterative Point Correspondence (AIPC) algorithm towards image registration is presented. Given an image pair, distinctive points are extracted only in one of the images (reference image), and the corresponding points in the other image are obtained automatically by maximizing a similarity measure between regions of the two images with respect to the parameters of a local transformation. The maximization is accomplished by means of an iterative procedure, in which candidate solutions for the transformation parameters are tested at each iteration; these solutions are evaluated by the similarity measure between image regions. The detected point pairs by the application of the AIPC algorithm are then used to estimate the parameters of a global projective transformation for the registration of the image pair. The proposed AIPC algorithm was applied on 113 in vitro and in vivo dental image pairs providing improved registration accuracy against three widely used registration methods. (c) 2008 Elsevier Ireland Ltd. All rights reserved. en
heal.publisher ELSEVIER IRELAND LTD en
heal.journalName Computer Methods and Programs in Biomedicine en
dc.identifier.doi 10.1016/j.cmpb.2008.07.003 en
dc.identifier.isi ISI:000263214200006 en
dc.identifier.volume 93 en
dc.identifier.issue 1 en
dc.identifier.spage 61 en
dc.identifier.epage 72 en

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