Probabilistic inference of regularisation in non-rigid registration

NeuroImage

Volumn 59, Issue 3, 2012, Pages 2438-2451

  Simpson, Ivor J A ^a,b   Schnabel, Julia A ^a   Groves, Adrian R ^b   Andersson, Jesper L R ^b   Woolrich, Mark W ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Bayesian modelling; Registration; Regularisation

Indexed keywords

ARTICLE; BAYES THEOREM; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DATA BASE; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE REGISTRATION; MATHEMATICAL MODEL; NUCLEAR MAGNETIC RESONANCE IMAGING; OPTICAL RESOLUTION; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; REGISTRATION; SIGNAL NOISE RATIO;

ALGORITHMS; BAYES THEOREM; BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; SIGNAL-TO-NOISE RATIO;

EID: 84855446283     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.09.002     Document Type: Article

References (36)

1. Allassonniére S., Amit Y., Trouvè A. Toward a coherent statistical framework for dense deformable template estimation. J. R. Stat. Soc. B 2007, 69.
2. Andersson J., Jenkinson M., Smith S. Non-linear registration, aka spatial normalisation. FMRIB technical report TR07JA1 from www.fmrib.ox.ac.uk/analysis/techrep (In the process of submission to NeuroImage) 2007.
3. Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage 2007, 38:95-113.
4. Ashburner J., Friston K.J. Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. 1999, 7:254-266.
5. Ashburner J., Friston K.J. Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 2011, 55:954-967.
6. Avants B., Epstein C., Grossman M., Gee J. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 2008, 12:26-41.
7. Beg M., Miller M., Trouvé A., Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 2005, 61:139-157.
8. Christensen G., Rabbitt R., Miller M. Deformable templates using large deformation kinematics. IEEE Trans. Image Process. 1996, 5:1435-1447.
9. Christensen G., Geng X., Kuhl J., Bruss J., Grabowski T., Pirwani I., Vannier M., Allen J., Damasio H. Introduction to the non-rigid image registration evaluation project (NIREP). volume 4057 of LNCS 2006, 128-135. Springer. J. Pluim, B. Likar, F. Gerritsen (Eds.).
10. Cox R. Probability, frequency and reasonable expectation. Am. J. Phys. 1946, 14:1-13.
11. Crum W., Hartkens T., Hill D. Non-rigid image registration: theory and practice. Br. J. Radiol. 2004, 77:S140.
12. Dempster A.P., Laird N.M., Rubin D.B., et al. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B (Methodological) 1977, 39:1-38.
13. Dice L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.
14. Groves A.R., Beckmann C.F., Smith S.M., Woolrich M.W. Linked independent component analysis for multimodal data fusion. NeuroImage 2011, 54:2198-2217.
15. Holden M. A review of geometric transformations for nonrigid body registration. IEEE Trans. Med. Imaging 2008, 27:111-128.
16. Hub M., Kessler M.L., Karger C.P. A stochastic approach to estimate the uncertainty involved in B-spline image registration. IEEE Trans. Med. Imaging 2009, 28:1708-1716.
17. Jaakkola T. Tutorial on variational approximation methods. Advanced mean field methods: theory and practice 2001, 129-160.
18. Jenkinson M., Smith S.M. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 2001, 5:143-156.
19. Jordan M.I., Ghahramani Z., Jaakkola T.S., Saul L.K. An introduction to variational methods for graphical models. Mach. Learn. 1999, 37:183-233.
20. Keller M., Roche A., Tucholka A., Thirion B. Dealing with spatial normalization errors in fMRI group inference using hierarchical modeling. Stat. Sin. 2008, 18:1357-1374.
21. Kullback S., Leibler R.A. On information and sufficiency. Ann. Math. Stat. 1951, 22:79-86.
22. Kybic J. Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Trans. Image Process. 2010, 19:64-73.
23. Peterson C., Anderson J.R. A mean field theory learning algorithm for neural networks. Complex Syst. 1987, 1:995-1019.
24. Risholm P., Pieper S., Samset E., Wells W. Summarizing and visualizing uncertainty in non-rigid registration. volume 6362 of LNCS 2010, 554-561. Springer. T. Jiang, N. Navab, J. Pluim, M. Viergever (Eds.).
25. Risholm P., Samset E., Wells W. Bayesian estimation of deformation and elastic parameters in non-rigid registration. Workshop on Biomedical Image Registration 2010, 104-115.
26. Roberts S.J., Penny W.D. Variational Bayes for generalized autoregressive models. IEEE Trans. Signal Process. 2002, 50:2245-2257.
27. Rueckert D., Sonoda L., Hayes C., Hill D., Leach M., Hawkes D. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 1999, 18:712-721.
28. Schnabel J., Rueckert D., Quist M., Blackall J., Castellano-Smith A., Hartkens T., Penney G., Hall W., Liu H., Truwit C. A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. Med. Image Comput. Comput. Assist. Interv. 2001, 2001:573-581.
29. Simpson I.J.A., Woolrich M.W., Groves A.R., Schnabel J.A. Longitudinal brain MRI analysis with uncertain registration. MICCAI 2011 2011, Springer, LNCS. G. Fichtinger, A. Martel, T. Peters (Eds.).
30. Vercauteren T., Pennec X., Perchant A., Ayache N. Symmetric log-domain diffeomorphic registration: a demons-based approach. volume 5241 of LNCS 2008, 754-761. Springer. D. Metaxas, L. Axel, G. Fichtinger, G. Székely (Eds.).
31. Witkin A.P. Scale-space filtering. Readings in computer vision: issues, problems, principles, and paradigms 1987, 329-332.
32. Woolrich M.W., Behrens T.E. Variational Bayes inference of spatial mixture models for segmentation. IEEE Trans. Med. Imaging 2006, 25:1380-1391.
33. Woolrich M.W., Behrens T.E.J., Smith S.M. Constrained linear basis sets for HRF modelling using variational Bayes. NeuroImage 2004, 21:1748-1761.
34. Worsley K.J., Poline J.B., Vandal A.C., Friston K.J. Tests for distributed, nonfocal brain activations. NeuroImage 1995, 2:183-194.
35. Yeo B.T.T., Sabuncu M.R., Vercauteren T., Holt D.J., Amunts K., Zilles K., Golland P., Fischl B. Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex. IEEE Trans. Med. Imaging 2010, 29:1424-1441.
36. Zöllei L., Jenkinson M., Timoner S., Wells W. A Marginalized MAP Approach and EM Optimization for Pair-wise Registration. Information Processing in Medical Imaging 2007, 662-674. Springer.