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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Non-local graph-based regularization for deformable image registration.
In: (Bayesian and grAphical Models for Biomedical Imaging). Berlin [u.a.]: Springer, 2017. 199-207 (Lect. Notes Comput. Sc. ; 10081 LNCS)
Deformable image registration aims to deliver a plausible spatial transformation between two or more images by solving a highly dimensional, ill-posed optimization problem. Covering the complexity of physiological motion has so far been limited to either generic physical models or local motion regularization models. This paper presents an alternative, graphical regularization model, which captures well the non-local scale of motion, and thus enables to incorporate complex regularization models directly into deformable image registration. In order to build the proposed graph-based regularization, a Minimum Spanning Tree (MST), which represents the underlying tissue physiology in a perceptually meaningful way, is computed first. This is followed by a fast non-local cost aggregation algorithm that performs regularization of the estimated displacement field using the precomputed MST. To demonstrate the advantage of the presented regularization, we embed it into the widely used Demons registration framework. The presented method is shown to improve the accuracy for exhale-inhale CT data pairs.
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Publikationstyp
Artikel: Konferenzbeitrag
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
Bayesian and grAphical Models for Biomedical Imaging
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 10081 LNCS,
Seiten: 199-207
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)