Registration of brain images by a multi-resolution sequential method

  • M A Oghabian
  • A Todd-Pokropek
3. Multi-Modal Registration
Part of the Lecture Notes in Computer Science book series (LNCS, volume 511)


In many circumstances the clinical interpretation of an imaging from a single image modality is inadequate. A number of registration techniques have been introduced in the literature in order to correlate the clinical information obtained from two different imaging modalities. All methods based on some distance measure suffer from the presence of multiple local minima when minimization algorithms are used to reduce the distance between two edges, or surfaces. A multi-resolution technique has been developed in conjunction with a sequentially improved distance function in order to register sets of MRI, PET, and SPECT images. A global search is initially performed on coarse resolution 3D surface images of each modality where a variable threshold is used to select any likely match location for finer resolution levels. An adaptive termination of the computation of the distance function is possible due to the sequential nature of its evaluation. The superimposed images of MRI and HMPAO images, displayed as slices and in 3-D, were clinically helpful.


Medical imaging multimodality imaging three dimensional graphics surface detection surface fitting superimposition minimization least square image display 


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  1. Abu-Mostafa Y S, Psaltis D (1984). Recognition aspects of moment invariants. IEEE Trans. Pattern Anal. Mach. Intel. Vol.6: 698–706Google Scholar
  2. Andrus J, Campbell C (1975). Digital image registration method using boundary maps: IEEE Trans. on Comp. Vol.19:935–940.Google Scholar
  3. Barnea D, Silverman H (1972). A class of algorithm for fast digital image registration. IEEE Trans. on Comp. Vol. C21, No.2: 179–186Google Scholar
  4. Bhanu D, Faugeras O (1984), Shape matching of two dimensional objects. IEEE Trans. Pat. Anal. Mach. Intel. Vol.6: 137–155Google Scholar
  5. Cappelletti J D, Rosenfeld A (1989). Three-Dimensional boundary following: Comp. Vision Grap. and image processing, Vol.48: 80–92.Google Scholar
  6. Chen C-Tu, Pelizzari C A, Chen G T Y (1988). Correlating Functional Nuclear Medicine Images with Structural CT or MR Images: Proceedings of 1988 meeting of Asian Society of Nuclear Medicine.Google Scholar
  7. Hu M K (1962). Visual pattern recognition by moment invariants: IRE Trans.on Information Theory. Vol.8: 179–187Google Scholar
  8. James F (1980). Monte carlo theory and practice: Rep. Prog. Phys. Vol.43: pp. 1173–1189.Google Scholar
  9. Kall B A, Kelly P J (1985). Cross registration of points and lesion volumes from MR and CT: IEEE 7th annual conference of the engineering in medicine and biology society. 937–942Google Scholar
  10. Maitra S (1979). Moment invariants: Proc.IEEE. Vol.67: 697–699Google Scholar
  11. Mandava V R, Fitzpatrick J M (1989). Adaptive search space scaling in digital image registration: IEEE Trans. on Med. Imag. Vol.8, No.3: 251–262Google Scholar
  12. Medioni S, Nevatia R (1984). Matching images using linear features: IEEE Trans. Pattern Anal. Mach. Intel. Vol.6:675–685.Google Scholar
  13. Merickel M, Mc Carthy M (1985). Registration of contours for 3D reconstruction: IEEE 7th annual conference of the engineering in medicine and biology society. 616–620Google Scholar
  14. Mitiche A, Aggarwal J K (1983). Contour registration by shape-specific points for shape matching. Comp. Vision Grap. and Image Processing. Vol.22: 396–408Google Scholar
  15. Nagel R N, Rosenfeld A (1972). Ordered search techniques in template matching: Proc. IEEE. Vol.60: pp.242–244.Google Scholar
  16. Pelizzari C A, Chen G T Y (1987). Registration of multiple diagnostic imaging scans using surface fitting: Proceeding of the 9th ICCR, pp.437–440.Google Scholar
  17. Pelizzari C A, Chen G T Y (1989). Accurate Three-Dimensional registration of CT, PET and NMR Images of the brain: J. of Comp. Assis. Tomog. Jan 1989.Google Scholar
  18. Powell M.J.D (1964). An efficient method of finding the minimum of a function of several variables without calculating derivatives: The Comp. J. Vol.7: 155–162Google Scholar
  19. Price K, Reddy R (1979). Matching segments of image: IEEE Trans. Pattern Anal. Mach. Intel. Vol.1: 110–116.Google Scholar
  20. Rosenfeld A, Vanderbrug G J (1977). Coarse-Fine template matching, IEEE Trans on Sys. Man and Cybernetics. Feb 1977: 104–107.Google Scholar
  21. Svedlow M, Gillem C D (1978). Image registration: Similarity measure and preprocessing method comparisons. IEEE Trans. Aerospace. Elec. Sys. Vol.14: 141–149Google Scholar
  22. Vanderbrug G J, Rosenfeld A (1977). Two-stage template matching, IEEE Trans. on Comp. Apr. Vol.C26, No.4:384–392.Google Scholar
  23. Wong R Y, Hall E L (1978). Sequential Hierarchical Scene Matching, IEEE Trans. on Comp. Apr 1978, Vol C27,No.4:359–366.Google Scholar
  24. Wong R Y (1978). Sequential scene matching using edge features, IEEE. Trans. on Aerospace and Elec. Sys. Jan 1978, Vol.Aes14, No.1: 128–140Google Scholar
  25. Wong R Y, Hall E L (1979). Performance comparison of scene matching techniques. IEEE Trans. on Pattern. Anal. & Mach. Intel. Jul 1979. Vol.PAMI-1, No.3: 325–330Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • M A Oghabian
    • 1
  • A Todd-Pokropek
    • 1
  1. 1.Department of Medical PhysicsUniversity College LondonLondonUK

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