Dense image registration through MRFs and efficient linear programming

Medical Image Analysis

Volumn 12, Issue 6, 2008, Pages 731-741

  Glocker, Ben ^a,b   Komodakis, Nikos ^a,c   Tziritas, Georgios ^c   Navab, Nassir ^b   Paragios, Nikos ^a,d  

^a Ecole Centrale de Paris   (France)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c UNIVERSITY OF CRETE   (Greece)

^d INRIA SACLAY   (France)

Author keywords

Deformable registration; Discrete optimization; Linear programming

Indexed keywords

COST FUNCTIONS; COSTS; DEFORMATION; IMAGE REGISTRATION; MARKOV PROCESSES;

DEFORMABLE REGISTRATION; DENSE IMAGE REGISTRATIONS; DIMENSIONALITY REDUCTION; DISCRETE OPTIMIZATION; INCREMENTAL APPROACH; MARKOV RANDOM FIELDS; NEIGHBORHOOD SYSTEMS; REGISTRATION PROBLEMS;

LINEAR PROGRAMMING;

ACCURACY; ANALYTICAL ERROR; ARTICLE; CONCEPTUAL FRAMEWORK; IMAGE ANALYSIS; INTERMETHOD COMPARISON; LINEAR SYSTEM; MATHEMATICAL MODEL; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; QUALITY CONTROL;

EID: 54249107530     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2008.03.006     Document Type: Article

References (39)

1. Besag J. On the statistical analysis of dirty pictures (with discussion). Journal of the Royal Statistical Society Series B 48 (1986) 259-302
2. Boykov Y., and Veksler O. Handbook of mathematical models in computer vision. Graph Cuts in Vision and Graphics: Theories and Applications (2005), Springer pp. 79-96
3. Cachier P., Bardinet E., Dormont D., Pennec X., and Ayache N. Iconic feature based nonrigid registration: the pasha algorithm. Computer Vision and Image Understanding 89 2-3 (2003) 272-298
4. Choi Y., and Lee S. Injectivity conditions of 2d and 3d uniform cubic b-spline functions. Graphical Models 62 6 (2000) 411-427
5. Chou P.B., and Brown C.M. The theory and practice of bayesian image labeling. International Journal of Computer Vision 4 3 (1990) 185-210
6. Davatzikos C., Prince J., and Bryan R. Image registration based on boundary mapping. IEEE Transactions on Medical Imaging 15 1 (1996) 112-115
7. Duncan J.C., and Ayache N. Medical image analysis: progress over two decades and the challenges ahead. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 1 (2000) 85-106
8. Fleet D.J. Measurement of Image Velocity (1992), Kluwer Academic Publishers, Norwell, MA, USA
9. Ford L., and Fulkerson D. Flows in Networks (1962), Princeton University Press
10. Christensen, G.E., 1994. Deformable shape models for anatomy. PhD thesis. Washington University.
11. Geman S., and Geman D. Stochastic relaxation gibbs distributions and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (1984) 721-741
12. Gerig, G., Jomier, M., Chakos, M., 2001. Valmet: a new validation tool for assessing and improving 3d object segmentations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2001).
13. Glocker, B., Komodakis, N., Paragios, N., Glaser, C., Tziritas, G., Navab, N., 2007. Primal/dual linear programming and statistical atlases for cartilage segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007), pp. 536-543.
14. Glocker, B., Komodakis, N., Paragios, N., Tziritas, G., Navab, N., 2007. Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming. In: Information Processing in Medical Imaging, Kerkrade, Netherlands, pp. 408-410.
15. Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., Navab, N., 2008. Optical flow estimation with uncertainties through dynamic MRFs. IEEE Conference in Computer Vision and Pattern Recognition (CVPR), in press.
16. In: Hajnal J., Hill D.L.G., and Hawkes D.J. (Eds). Medical Image Registration (2001), CRC Press
17. Hellier P., and Barillot C. Coupling dense and landmark-based approaches for nonrigid registration. IEEE Transactions on Medical Imaging 22 2 (2003) 217-227
18. Hermosillo G., Chefd'hotel C., and Faugeras O. Variational methods for multimodal image matching. International Journal Computer Vision 50 3 (2002) 329-343
19. Kirkpatrick S., Gelatt C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 4598 (1983) 671-680
20. Klein S., Staring M., and Pluim J. Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines. IEEE Transactions on Image Processing 16 12 (2007) 2879-2890
21. Kolmogorov V., and Zabih R. What energy functions can be minimized via graph cuts?. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 2 (2004) 147-159
22. Komodakis N., and Tziritas G. Approximate labeling via graph-cuts based on linear programming. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (2007)
23. Komodakis, N., Tziritas, G., Paragios, N., 2007. Fast, approximately optimal solutions for single and dynamic mrfs. IEEE Computer Vision and Pattern Recognition Conference (CVPR), pp. 1-8.
24. Li S.Z. Markov random field modeling in image analysis (2001), Springer Verlag Inc., New York
25. Loeckx D., Maes F., Vandermeulen D., and Suetens P. Nonrigid image registration using free-form deformations with a local rigidity constraint. Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2004) 639-646
26. Maes F., Collignon A., Vandermeulen D., Marchal G., and Suetens P. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16 2 (1997) 187-198
27. Paragios N., Duncan J., and Ayache N. Biomedical Image Analysis: Methodologies and Applications (2009), Springer, USA
28. Pennec, X., Ayache, N., Thirion, J.-P., 2000. Handbook of medical imaging. In: Landmark-based Registration using Features Identified Through Differential Geometry, pp. 499-513.
29. Roche, A., Malandain, G., Pennec, X., Ayache, N., 1998. The correlation ratio as a new similarity measure for multimodal image registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer-Verlag, pp. 1115-1124.
30. Rohlfing T., Maurer C.R., Bluemke D., and Jacobs M. Volume-preserving nonrigid registration of mr breast images using free-form deformation with an incompressibility constraint. IEEE Transactions on Medical Imaging 22 6 (2003) 730-741
31. Rohr K., Fornefett M., and Stiehl H.S. Spline-based elastic image registration: integration of landmark errors and orientation attributes. Computer Vision and Image Understanding 90 2 (2003) 153-168
32. Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J., Hammers, A., 2006. Diffeomorphic registration using b-splines. In: Proceedings of 9th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2006), October 2006.
33. Rueckert D., Sonoda L., Hayes C., Hill D., Leach M., and Hawkes D. Nonrigid registration using free-form deformations: application to breast mr images. IEEE Transactions on Medical Imaging 18 8 (1999) 712-721
34. Sederberg, T., Parry, S., 1986. Free-form deformation of solid geometric models, In: Proceedings of ACM SIGGRAPH, pp. 151-159.
35. Schnabel J.A., Rueckert D., Quist M., Blackall J.M., Castellano-Smith A.D., Hartkens T., Penney G.P., Hall W.A., Liu H., Truwit C.L., Gerritsen F.A., Hill D.L.G., and Hawkes D.J. A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2001)
36. Tikhonov, A., 1992. Ill-posed problems in natural sciences. Coronet.
37. Veksler, O., 1999. Efficient graph-based energy minimization methods in computer vision. PhD thesis. Graduate School of Cornell University. Adviser-Ramin Zabih.
38. Viola P., and Wells W.M. Alignment by maximization of mutual information. International Journal of Computer Vision 24 2 (1997) 137-154
39. Zollei L., Fisher J., and Wells W. Handbook of mathematical models in computer vision. An Introduction to Statistical Methods of Medical Image Registration (2005), Springer