Medical imaging

Registration - Fusion (Matching of two modalities)

The aim of matching is the ability to use more than one modality, i.e. to combine modalities in order to specify with better accuracy, the target, the catheters and any other critical structure (e.g., organs at risk and gross tumor volumes). The process of image registration can be formulated as a problem of optimizing a function that quantifies the match between the images of two modalities. In order to find this function different common features of these images can be used such as: points visible in both modalities (fiducial markers or landmarks), outer contour of different structures (e.g. body contour) or the voxel intensity values.

In our research a number of diferent techniques has been investigated in order to match the surface of the two body contours. The SVD (Singular Value Decomposition), the distance map or the ICP (Iterative Closest Point) method of the outer body contour as well as different global optimization methods (e.g., Downhill Simplex, Powell, Simulated Annealing).

The results of our research in this field was used in commercial products.



The problem of 3D surface reconstruction from oriented points over slices is difficult and a lot of different approaches have been proposed by the scientific community. Out of them, a number of promising methods have been impleneted and tested with many improvements from the in-house research effort.
Some of the algorithms developed in house have been used in commercial products.







  1. N.M. Alpert, J.F. Bradshaw, D. Kennedy, and J.A. Correia, "The principal axes transformation- a method for image registration", Journal of Nuclear Medicine, vol. 31, no. 10, pp. 1717-1722, 1990.
  2. K. S. Arun, T. S. Huang, AND S. D. Blostein. "Least Squares Fitting of Two 3-D Point Sets", IEEE Trans. Patt. Anal. and Mach. Intell., vol. PAMI-9, no. 5, September 1987.
  3. P. Besl, N. McKay. "A Method for Registration of 3-D Shapes", IEEE Trans. Patt. Anal. and Mach. Intell., vol. 14, no. 2, February 1992.
  4. D. Hill, D. Hawkes, J. Crossman, M. Gleeson, T. Cox et al, "Registration of MR and CT images for skull base surgery using point-like anatomical features", British Journal of Radiology, vol. 64, pp. 1030-1035, 1991.
  5. D. Kozinska, O.J. Tretiak, and J. Nissanov, "Multidimensional alignment using Euclidean distance transform", Graphical Models and Image Processing, vol. 59, no. 6, pp. 373-387, 1997.
  6. F. Maes, A. Collignon, D. Vandermeulen, P. Suetens, "Multimodality Image Registration by Maximization of Mutual Information", IEEE Trans. Med. Imag., vol. 16, no. 2, April 1997.
  7. J. Maguire, M. Noz, H. Rusinek, J. Jaeger, E.L. Kramer, et al, "Graphics applied to medical image registration", IEEE. Comp. Graph. Appl., vol. 11, no. 2, pp. 20-28, 1991
  8. H. Fuchs, Z. M. Kedem, S. P. Uselton. "Optimal Surface Reconstruction from Planar Contours", Communications of the ACM, Vol. 20, No. 10, 693-702, 1977.
  9. R. Seidel, "A simple and Fast Randomized Algorithm for Computing Trapezoidal Decompositions and for Triangulating Polygons",Computational Geometr Theory & Applications,51-64, 1,(1991)