Abstract
We present a new approach for the non-rigid registration of contrast-enhanced breast MRI using normalised mutual information. A hierarchical transformation model of the motion of the breast has been developed: The global motion of the breast is modelled using affine transformation models while the local motion of the breast is modelled using spline-based free-form deformation (FFD) models. The algorithm has been applied to the fully automated registration of 3D breast MRI. In particular, we have compared the results of the proposed non-rigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the non-rigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
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Keywords
- Mutual Information
- Transformation Model
- Affine Transformation
- Normalise Mutual Information
- Registration Algorithm
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
C. Studholme, D. L. G. Hill, and D. J. Hawkes. Multiresolution voxel similarity measures for MR-PET registration. In Information Processing in Medical Imaging: Proc. 14th International Conference (IPMI’95), pages 287–298, 1995.
A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Seutens, and G. Marchal. Automated multimodality image registration using information theory. In Information Processing in Medical Imaging: Proc. 14th International Conference (IPMI’95), pages 263–274, 1995.
P. Viola. Alignment By Maximization of Mutual Information. PhD thesis, Massachusetts Institute of Technology. A.I. Technical Report No. 1548., 1995.
P. Viola and W. M. Wells. Alignment by maximization of mutual information. International Journal of Computer Vision, 24(2):137–154, 1997.
C. Studholme, D. L. G. Hill, and D. J. Hawkes. Automated 3-D registration of MR and CT images of the head. Medical Image Analysis, 1(2):163–175, 1996.
C. Studholme. Measures of 3D Medical Image Alignment. PhD thesis, United Medical and Dental Schools of Guy’s and St. Thomas’s Hospitals, 1997.
C. Studholme, D. L. G. Hill, and D. J. Hawkes. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 1998. To appear.
F. Maes, A. Collignon, D. Vandermeulen, G. Marechal, and R. Suetens. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2):187–198, 1997.
C. R. Meyer, J. L. Boes, B. Kim, P. H. Bland, K. R. Zasadny, P. V. Kison, K. Koral, K. A. Frey, and R. L. Wahl. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Medical Image Analysis, 1(3):195–207, 1997.
C. S. Zuo, A. P. Jiang, B. L. Buff, T. G. Mahon, and T. Z. Wong. Automatic motion correction for breast MR imaging. Radiology, 198(3):903–906, 1996.
R. Kumar, J. C. Asmuth, K. Hanna, J. Bergen, C. Hulka, D. B. Kopans, R. Weisskoff, and R. Moore. Application of 3D registration for detecting lesions in magnetic resonance breast scans. In Proc. SPIE Medical Imaging 1996: Image Processing, volume 2710, pages 646–656, Newport Beach, USA, February 1996. SPIE.
P. Hayton, M. Brady, L. Tarassenko, and N. Moore. Analysis of dynamic MR breast images using a model of contrast enhancement. Medical Image Analysis, 1(3):207–224, 1997.
R. Szelski and S. Lavallee. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines. In IEEE Workshop on Biomedical Image Analysis, pages 144–153, 1994.
S. Lee, G. Wolberg, K.-Y. Chwa, and S. Y. Shin. Image metamorphosis with scattered feature constraints. IEEE Transactions on Visualization and Computer Graphics, 2(4):337–354, 1996.
S. Lee, G. Wolberg, and S. Y Shin. Scattered data interpolation with multilevel B-Splines. IEEE Transactions on Visualization and Computer Graphics, 3(3):228–244, 1997.
G. Wahba. Spline Models for Observational Data. Society for Industrial and Applied Mathematics, 1990.
F. L. Bookstein. Principal Warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567–585, 1989.
M. H. Davis, A. Khotanzad, D. P. Flamig, and S. E. Harms. A physics-based coordinate transformation for 3-D image matching. IEEE Transactions on Medical Imaging, 16(3):317–328, 1997.
P. J. Edwards, D. L. G. Hill, J. A. Little, and D. J. Hawkes. Deformation for image-guided interventions using a three-component tissue model. In Information Processing in Medical Imaging: Proc. 15th International Conference (IPMI’97), pages 218–231, 1997.
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© 1998 Springer-Verlag Berlin Heidelberg
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Rueckert, D., Hayes, C., Studholme, C., Summers, P., Leach, M., Hawkes, D.J. (1998). Non-rigid registration of breast MR images using mutual information. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056304
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DOI: https://doi.org/10.1007/BFb0056304
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