Non-rigid Registration for Colorectal Cancer MR Images

  • Sarah L. Bond
  • J. Michael Brady
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)


We are developing a system for patient management in colorectal cancer, in which the need for segmentation and non-rigid registration of pre- and post-therapy images arises. Several methods for non-rigid registration have been proposed, all of which embody a ’generic’ algorithm to solve registration, largely irrespective both of the kinds of images and of the application. We have evaluated several of these algorithms for this application and find their performance unsuitable for aligning pre- and post- therapy colorectal images. This leads us to identify some of the implicit assumptions and fundamental limitations of these algorithms. None of the currently available algorithms take into account the issue of scale salience and more importantly, none of the algorithms ”know” enough about colorectal MRI to focus their attention for registration on those parts of the image that are clinically important. Based on this analysis, we propose a way in which we can perform registration by mobilizing the knowledge of the particular application, for example the prior shape knowledge that we have within the colorectal images as well as knowledge of the large scale non-rigid changes due to therapy.


Mutual Information Breast Magnetic Resonance Image Registration Algorithm Weighted Magnetic Resonance Patient Management Decision 
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.


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  1. 1.
    Bond, S., Brady, M.: Image Analysis for Patient Management in Colorectal Cancer. In: To be presented at CARS 2005, Berlin (June 2005)Google Scholar
  2. 2.
    Styner, M., Brechbuhler, C., Szekely, G., Gerig, G.: Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans. Med. Imag. 19, 153–165 (2000)CrossRefGoogle Scholar
  3. 3.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans. Med. Imag. 18, 712–721 (1999)CrossRefGoogle Scholar
  4. 4.
    Park, H., Bland, P.H., Brock, K.K., Meyer, C.R.: Adaptive Registration Using Local Information Measures. Med. Imag. Anal. 8, 465–473 (2004)CrossRefGoogle Scholar
  5. 5.
    Crum, W.R., Hill, D.L.G., Hawkes, D.J.: Information Theoretic Similarity Measures in Non-rigid Registration. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 378–387. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Kadir, T., Brady, J.M.: Saliency, Scale and Image Description. Int. J. Comp. Vision 45, 83–105 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Chen, X., Brady, M., Lo, J., Moore, N.: Simultaneous Segmentation and Registration of Contrast-Enhanced Breast MRI. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 126–137. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Christensen, G., Carlson, B., Chao, K., et al.: Image-based Dose Planning of Intracavity Brachytherapy: Registration of Serial-Imaging Studies Using deformable Anatomic Templates. Int. J. Radiation Oncology Biol. Phys. 51, 227–243 (2001)Google Scholar
  10. 10.
    Thirion, J., Calmon, G.: Deformation Analysis to Detect and Quantify Active Lesion in 3D Medical Image Sequences. Research Report 3101. INRIA (1997)Google Scholar
  11. 11.
    Rey, D., Subsol, G., Delingette, H., Ayache, N.: Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 154–167. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Studhome, C., Hill, D., Hawkes, D.: An Overlap Invariant Entropy Measureof 3D Medical Image Alignment. Pattern Recognit 32, 71–86 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sarah L. Bond
    • 1
  • J. Michael Brady
    • 1
  1. 1.Wolfson Medical Vision Laboratory, Department of Engineering ScienceUniversity of OxfordOxfordUK

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