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Schnabel, J.A.* ; Tanner, C.* ; Smith, A.D.C.* ; Leach, M.O.* ; Hayes, C.* ; Degenhard, A.* ; Hose, R.* ; Hill, D.L.G.* ; Hawkes, D.J.*

Validation of non-rigid registration using finite element methods.

In: (Biennial International Conference on Information Processing in Medical Imaging). Berlin [u.a.]: Springer, 2001. 344-357 (Lect. Notes Comput. Sc. ; 2082)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We present a novel validation method for non-rigid registration using a simulation of deformations based on biomechanical modelling of tissue properties. This method is tested on a previously developed non-rigid registration method for dynamic contrast enhanced Magnetic Resonance (MR) mammography image pairs [1]. We have constructed finite element breast models and applied a range of displacements to them, with an emphasis on generating physically plausible deformations which may occur during normal patient scanning procedures. From the finite element method (FEM) solutions, we have generated a set of deformed contrast enhanced images against which we have registered the original dynamic image pairs. The registration results have been successfully validated at all breast tissue locations by comparing the recovered displacements with the biomechanical displacements. The validation method presented in this paper is an important tool to provide biomechanical gold standard deformations for registration error quantification, which may also form the basis to improve and compare different non-rigid registration techniques for a diversity of medical applications.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Biennial International Conference on Information Processing in Medical Imaging
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 2082, Heft: , Seiten: 344-357 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)