Abstract
Fusing of multi-modal data involves automatically estimating the coordinate transformation required to align the data sets. Most existing methods in literature are not robust and fast enough for practical use. We propose a robust algorithm, based on matching local-frequency image representations, which naturally allow for processing the data at different scales/resolutions, a very desirable property from a computational efficiency view point. This algorithm involves minimizing — over all affine transformations — the integral of the squared error (ISE or L 2E) between a Gaussian model of the residual and its true density function. The residual here refers to the difference between the local frequency representations of the transformed (by an unknown transformation) source and target data. The primary advantage of our algorithm is its ability to cope with large non-overlapping fields of view of the two data sets being registered, a common occurrence in practise. We present implementation results for misalignments between CT and MR brain scans.
Acknowledgments
We thank Drs. Bova & Bouchet and Mr. Moore for providing the image data. This research was partially supported by the grants NSF IIS9811042 and NIH RO1-RR13197.
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References
R. Bansal, et.al., [1998], “A novel approach for registration of 2D portal and 3D CTim ages for treatment setup verification in radiotherapy,” in Proc. of MICCAI, Cambridge, MA, 1075–1086.
D. P. Bertsekas, [1999], Nonlinear Programing, Athena Scientific Publishers.
A. Collignon, et.al., (1995) Automated multimodality image registration using information theory, In Proc. of IPMI, 263–274.
G.H. Granlund and H. Knutsson, [1995], Signal Processing for Computer Vision. Kluwer, Netherlands.
M. E. Leventon and W. E. L. Grimson, [1998], “Multimodal volume registration using joint intensity distributions,” Proc. of MICCAI, Cambridge, MA, 1057–1066.
J.B. Maintz and M. A. Viergever, [1998], “A Survey of Medical Image Registration,” MedIA,2, 1–36.
J. L. Marroquin, et.al., 1997, “Adaptive quadrature filters and the recovery of phase from fringe pattern images,” JOSA, 14(8), 1742–1752.
C. T. Meyer, et. al., [1997], Demonstrating the accuracy and clinical versatility of MI...MedIA,1(3), 195–206.
D. W. Scott, “Parametric modeling by minimum L 2 error,” Technical Report 98-3, Dept. of Stat., Rice University.
B. C. Vemuri et. al., [1998], An efficient motion estimator with application to medical image registration, MedIA, 2(1), 79–98.
B. C. Vemuri et.al., [2000], “A Level-set based approach to image registration,” IEEE Workshop on MMBIA, June, Hilton Head, SC.
P. A. Viola and W. M. Wells (1995), Alignment by maximization of mutual information, in Fifth ICCV, MIT, Cambridge, MA, 16–23.
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Vemuri, B.C., Liu, J., Marroquin, J.L. (2001). Robust Multimodal Image Registration Using Local Frequency Representations. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_17
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DOI: https://doi.org/10.1007/3-540-45729-1_17
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