Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-85988-8_92
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dc.titleNonrigid registration of dynamic renal MR images using a saliency based MRF model
dc.contributor.authorMahapatra, D.
dc.contributor.authorSun, Y.
dc.date.accessioned2014-06-19T03:20:38Z
dc.date.available2014-06-19T03:20:38Z
dc.date.issued2008
dc.identifier.citationMahapatra, D.,Sun, Y. (2008). Nonrigid registration of dynamic renal MR images using a saliency based MRF model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5241 LNCS (PART 1) : 771-779. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-540-85988-8_92" target="_blank">https://doi.org/10.1007/978-3-540-85988-8_92</a>
dc.identifier.isbn354085987X
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71165
dc.description.abstractNonrigid registration of contrast-enhanced MR images is a difficult problem due to the change in pixel intensity caused by the wash-in and wash-out of the contrast agent. In this paper we propose a novel saliency based Markov Random Field approach for effective nonrigid registration of contrast enhanced images. Saliency information obtained from the neurobiology-based saliency model alongwith intensity information is used to quantify the degree of similarity between images in the pre- and post-contrast stages. Information from these two features is combined by using an exponential function of the saliency difference such that it assigns low values to small differences in saliency and at the same time ensures that saliency information does not bias the energy term. Rotationally-invariant edge information from edge-orientation histograms was used to complement the saliency information resulting in better registration results. Tests on real patient datasets show that our algorithm results in accurate registration. We also simulated elastic motion on images, and the deformation field recovered by our algorithm was nearly the inverse of the simulated field. © 2008 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-540-85988-8_92
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-540-85988-8_92
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5241 LNCS
dc.description.issuePART 1
dc.description.page771-779
dc.identifier.isiutNOT_IN_WOS
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