A Unifying Framework for Correspondence-Less Shape Alignment and Its Medical Applications

  • Zoltan Kato
Part of the Communications in Computer and Information Science book series (CCIS, volume 276)


We give an overview of our general framework for registering 2D and 3D objects without correspondences. Classical solutions consist in extracting landmarks, establishing correspondences and then the aligning transformation is obtained via a complex optimization procedure. In contrast, our framework works without landmark correspondences, is independent of the magnitude of transformation, easy to implement, and has a linear time complexity. The efficiency and robustness of the method has been demonstarted using various deformations models. Herein, we will focus on medical applications.


Registration Shape 3D Object Affine transformation Thin plate splines Bone Fracture Prostate MRI TRUS 


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  1. 1.
    Holden, M.: A review of geometric transformations for nonrigid body registration. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 111–128 (2008)Google Scholar
  2. 2.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape context. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 509–522 (2002)CrossRefGoogle Scholar
  3. 3.
    Heikkilä, J.: Pattern matching with affine moment descriptors. Pattern Recognition 37, 1825–1834 (2004)MATHCrossRefGoogle Scholar
  4. 4.
    Hagege, R., Francos, J.M.: Parametric estimation of multi-dimensional affine transformations:an exact linear solution. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, USA, vol. 2, pp. 861–864. IEEE (2005)Google Scholar
  5. 5.
    Domokos, C., Kato, Z.: Parametric estimation of affine deformations of planar shapes. Pattern Recognition 43, 569–578 (2010)MATHCrossRefGoogle Scholar
  6. 6.
    Tanács, A., Domokos, C., Sladoje, N., Lindblad, J., Kato, Z.: Recovering Affine Deformations of Fuzzy Shapes. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 735–744. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Tanács, A., Sladoje, N., Lindblad, J., Kato, Z.: Estimation of linear deformations of 3D objects. In: Proceedings of International Conference on Image Processing, Hong Kong, China, pp. 153–156. IEEE (2010)Google Scholar
  8. 8.
    Tanacs, A., Kato, Z.: Fast linear registration of 3D objects segmented from medical images. In: Proceedings of International Conference on BioMedical Engineering and Informatics, Shanghai, China, pp. 299–303. IEEE (2011)Google Scholar
  9. 9.
    Domokos, C., Kato, Z.: Affine Puzzle: Realigning Deformed Object Fragments without Correspondences. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 777–790. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Domokos, C., Nemeth, J., Kato, Z.: Nonlinear shape registration without correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 943–958 (2012)CrossRefGoogle Scholar
  11. 11.
    Santa, Z., Kato, Z.: Elastic registration of 3D deformable objects. In: Proceedings of International Conference on Digital Image Computing: Techniques and Applications, Fremantle, Australia. IEEE (2012)Google Scholar
  12. 12.
    Santa, Z., Kato, Z.: A unifying framework for non-linear registration of 3D objects. In: Proceedings of International Conference on Cognitive Infocommunications, Kassa, Slovakia, pp. 547–552. IEEE (2012)Google Scholar
  13. 13.
    Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.H.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 526–534 (2001)Google Scholar
  14. 14.
    Mitra, J., Kato, Z., Marti, R., Oliver, A., Llado, X., Sidibe, D., Ghose, S., Vilanova, J.C., Comet, J., Meriaudeau, F.: A spline-based non-linear diffeomorphism for multimodal prostate registration. Medical Image Analysis 16, 1259–1279 (2012)CrossRefGoogle Scholar
  15. 15.
    Hagege, R., Francos, J.M.: Parametric estimation of affine transformations: An exact linear solution. Journal of Mathematical Imaging and Vision 37, 1–16 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Downing, M., Undrill, P., Ashcroft, P., Hukins, D., Hutchison, J.: Automated femoral measurement in total hip replacement radiographs. In: Proceedings of International Conference on Image Processing and Its Applications, Dublin, Ireland, vol. 2, pp. 843–847. IEEE (1997)Google Scholar
  17. 17.
    Hardinge, K., Porter, M.L., Jones, P.R., Hukins, D.W.L., Taylor, C.J.: Measurement of hip prostheses using image analysis. the maxima hip technique. Journal of Bone and Joint Surgery 73-B, 724–728 (1991)Google Scholar
  18. 18.
    Florea, C., Vertan, C., Florea, L.: Logarithmic Model-Based Dynamic Range Enhancement of Hip X-Ray Images. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 587–596. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Oprea, A., Vertan, C.: A quantitative evaluation of the hip prosthesis segmentation quality in x-ray images. In: Proceedings of International Symposium on Signals, Circuits and Systems, Iasi, Romania, vol. 1, pp. 1–4. IEEE (2007)Google Scholar
  20. 20.
    Erdőhelyi, B., Varga, E.: Semi-automatic bone fracture reduction in surgical planning. In: Proceedings of the International Conference on Computer Assisted Radiology and Surgery. International Journal of Computer Assisted Radiology and Surgery, vol. 4, pp. S98–S99. Springer, Berlin (2009)Google Scholar
  21. 21.
    Winkelbach, S., Westphal, R., Goesling, T.: Pose Estimation of Cylindrical Fragments for Semi-automatic Bone Fracture Reduction. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 566–573. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  22. 22.
    Pettersson, J., Knutsson, H., Borga, M.: Non-rigid registration for automatic fracture segmentation. In: Proceedings of International Conference on Image Processing, Atlanta, GA, USA, pp. 1185–1188. IEEE (2006)Google Scholar
  23. 23.
    Andriole, G.L., Crawford, E.D., Grubb, R.L., Buys, S.S., Chia, D., Church, T.R., Fouad, M.N., Gelmann, E.P., Reding, D.J., Weissfeld, J.L., Yokochi, L.A., O’Brien, B., Clapp, J.D., Rathmell, J.M., Riley, T.L., Hayes, R.B., Kramer, B.S., Izmirlian, G., Miller, A.B., Pinsky, P.F., Prorok, P.C., Gohagan, J.K., Berg, C.D.: Mortality results from a randomized prostate-cancer screening trial. The New England Journal of Medicine 360, 1310–1319 (2009)CrossRefGoogle Scholar
  24. 24.
    Carroll, P., Shinohara, K.: Transrectal ultrasound guided prostate biopsy. Technical report, Department of Urology, University of California, San Francisco (2010), http://urology.ucsf.edu/patientGuides.html (accessed December 30, 2010)
  25. 25.
    Vilanova, J.C., Barceló-Vidal, C., Comet, J., Boada, M., Barceló, J., Ferrer, J., Albanell, J.: Usefulness of prebiopsy multi-functional and morphologic MRI combined with the free-to-total PSA ratio in the detection of prostate cancer. American Journal of Roentgenology 196, W715–W722 (2011) CrossRefGoogle Scholar
  26. 26.
    Hu, Y., Ahmed, H.U., Taylor, Z., Allem, C., Emberton, M., Hawkes, D., Barratt, D.: MR to ultrasound registration for image-guided prostate interventions. Medical Image Analysis (2011) (in press), doi:10.1016/j.media.2010.11.003Google Scholar
  27. 27.
    Mitra, J., Srikantha, A., Sidibé, D., Martí, R., Oliver, A., Lladó, X., Vilanova, J.C., Meriaudeau, F.: A shape-based statistical method to retrieve 2D TRUS-MR slice correspondence for prostate biopsy. In: Proc. of SPIE Medical Imaging, San Diego, Calfornia, USA, vol. 8314, pp. 83143M-1–83143M-9 (2012)Google Scholar
  28. 28.
    Bryant, A.S., Cerfolio, R.J.: The maximum standardized uptake values on integrated FDG-PET/CT is useful in differentiating benign from malignant pulmonary nodules. The Annals of Thoracic Surgery 82, 1016–1020 (2006)CrossRefGoogle Scholar
  29. 29.
    Bookstein, F.L.: Principal warps: Thin-Plate Splines and the Decomposition of deformations. IEEE Transactions on Pattern Analyis and Machine Intelligence 11, 567–585 (1989)MATHCrossRefGoogle Scholar
  30. 30.
    Zagorchev, L., Goshtasby, A.: A comparative study of transformation functions for nonrigid image registration. IEEE Transactions on Image Processing 15, 529–538 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zoltan Kato
    • 1
  1. 1.Department of Image Processing and Computer GraphicsUniversity of SzegedSzegedHungary

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