Abstract
The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. We aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a Maximum a Posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data.
PW gratefully acknowledges the financial support of the UK EPSRC for funding this research.
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Keywords
- Mutual Information
- Gaussian Mixture Model
- Iterate Conditional Mode
- Medical Image Registration
- Joint Segmentation
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References
R Bansal and LH Staib et al., A Novel Approach for the Registration of 2D Portal and 3D CT Images for Treatment Setup Verification in Radiotherapy, MICCAI 1998, p1075–1086
R Bansal and LH Staib et al., Entropy-Based Multiple Portal to 3D CT Registration for Prostate Radiotherapy Using Iteratively Estimated Segmentation, MICCAI 1999, p567–578
J Besag, On the Statistical Analysis of Dirty Pictures, Journal of the Royal Statistical Society-Series B 48(3), p259–302, 1986
J Besag, Spatial Interaction and the Statistical Analysis of Lattice Systems, Journal of the Royal Statistical Society-Series B(36), p192–236, 1974
C Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995
S Geman and D Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE PAMI 1984, 6(6), p721–741.
DM Greig and BT Porteos and AH Seheult, Exact Maximum A Posteriori Estimation for Binary Images, Journal of the Royal Statistical Society-Series B 51(2), p271–279, 1989
YG Leclerc, Constructing Simple Stable Descriptions for Image Partitioning, IJCV, (3) p73–102, 1989
JBA Maintz and MA Viergever, A Survey of Medical Image Registration Methods, Medical Image Analysis, 2(1), p1–36, 1998
A Roche and G Malandain and N Ayache, Unifying Maximum Likelihood Approaches in Medical Image Registration, INRIA France, Tech Report RR-3741, 1999
WH Press and SA Teukolsky et al, Numerical Recipes in C, Cambridge University Press, 1992
A Yezzi and L Zollei and T Kapur A Variational Framework for Joint Segmentation and Registration IEEE Proc. MMBIA, p44–52, 2001
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© 2002 Springer-Verlag Berlin Heidelberg
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Wyatt, P.P., Noble, J.A. (2002). MAP MRF Joint Segmentation and Registration. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_72
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DOI: https://doi.org/10.1007/3-540-45786-0_72
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