Abstract
In medical image analysis there is frequently a need to invert dense displacement fields which map one image space to another. In this paper we describe inversion techniques and determine their accuracy in the context of 18 inter-subject brain image registrations. Scattered data interpolation (SDI) is used to initialise locally and globally consistent iterative techniques. The inverse-consistency error, E IC is computed over the whole image and over 10 specific brain regions. SDI produced good results with mean (max) E IC ~0.02mm (2.0mm). Both iterative method produced mean errors of ~0.005mm but the globally consistent method resulted in a smaller maximum error (1.9mm compared with 1.4mm). The largest errors were in the cerebral cortex with large outlier errors in the ventricles. Simple iterative techniques are, on this evidence, able to produce reasonable estimates of inverse displacement fields provided there is good initialisation.
This work is supported by the EPSRC Integrated Brain Image Modelling, Medical Image and Signals IRC and Modelling and Understanding and Predicting Structural Brain Change projects We are thankful to David Kennedy, Centre for Morphometric Analysis, MGH, Boston for the brain images used in this work.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Christensen, G.E, Johnson, H.J: Consistent image registration. IEEE Transactions on Medical Imaging 20(7), 568–582 (2001)
Cachier, P., Rey, D.: Symmetrization of the non-rigid registration problem using invertion-invariant energies: application to multiple sclerosis. In: Medical Image Computing and Computer-Assisted Intervention, pp. 472–481 (2000)
Rogelj, P., Kovačič, S.R.: Symmetric image registration. Medical Image Analysis 10(3), 484–493 (2006)
Crum, W.R, Griffin, L.D., Hill, D.L., Hawkes, D.J: Zen and the art of medical image registration: Correspondence, homology and quality. NeuroImage 20, 1425–1437 (2003)
Rao, A., Chandrashekara, R., Sanchez-Ortiz, G.I., Mohiaddin, R., Aljabar, P., Hajnal, J.V., Puri, B.K., Rueckert, D.: Spatial transformation of motion and deformation fields using nonrigid registration. IEEE Transactions on Medical Imaging 23(9), 1065–1076 (2004)
Karacali, B., Davatzikos, C.: Estimating topology preserving and smooth displacement fields. Journal of Electronic Imaging 23(7), 868–880 (2004)
Amidror, I.: Scattered data interpolation methods for electronic imaging systems: a survey. Journal of Electronic Imaging 11(2), 157–176 (2002)
Crum, W.R, Tanner, C., Hawkes, D.J: Multiresolution, anisotropic fluid registration: Evaluation in magnetic resonance breast imaging. Physics in Medicine and Biology 50, 5153–5174 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Crum, W.R., Camara, O., Hawkes, D.J. (2007). Methods for Inverting Dense Displacement Fields: Evaluation in Brain Image Registration. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75757-3_109
Download citation
DOI: https://doi.org/10.1007/978-3-540-75757-3_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75756-6
Online ISBN: 978-3-540-75757-3
eBook Packages: Computer ScienceComputer Science (R0)