A Localization Framework under Non-rigid Deformation for Robotic Surgery

  • Xiang Xiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


In surgery, it is common to open large incisions to remove tiny tumors. Now, robotic surgery has been well recognized for high precision. However, target localization is still a challenge, owing to non-rigid deformations. Thus, we propose a precise and flexible localization framework for an MRI-compatible needle-insertion robot. We primarily address with two problems: 1) How to predict the position after deformation? 2) How to turn MRI coordinate to real-world one? Correspondingly, the primary novelty is the non-rigid position transformation model based on Thin-Plate Splines. A minor contribution lies in the data acquisition for coordinate correspondences. We validate the precision of the whole framework, and each procedure of coordinate acquisition and position transformation. It is proven that the system under our framework can predict the position with a good approximation to the target’s real position.


Robotic Surgery Grape Fruit Localization Framework Medical Image Registration Position Transformation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dimaio, S., Salcudean, S.: Needle Insertion Modelling and Simulation. In: Proc. IEEE ICRA (2002)Google Scholar
  2. 2.
    Yamauchi, Y., Ohta, Y., Dohi, T., Kawamura, H., Tanikawa, T., Isekim, H.: A Needle Insertion Manipulator for X-ray CT Image Guided Neurosurgery. J. of LST 5-4, 814–821 (1993)Google Scholar
  3. 3.
    Masamune, K., Kobayashi, E., Masutani, Y., Suzuki, M., Dohi, T., Iseki, H., Takakura, K.: Development of an MRI-compatible Needle Insertion Manipulator for Stereotactic Neurosurgery. J. Image Guid. Surg. 1(4), 242–248 (1995)CrossRefGoogle Scholar
  4. 4.
    Chinzei, K., Hata, N., Jolesz, F., Kikinis, R.: MR Compatible Surgical Assist Robot: System Integration and Preliminary Feasibility Study. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 921–930. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Fischer, G., Iordachita, I., Csoma, C., Tokuda, J., Mewes, P., Tempany, C., Hata, N., Fichtinger, G.: Pneumatically Operated MRI-Compatible Needle Placement Robot for Prostate Interventions. In: Proc. IEEE ICRA (2008)Google Scholar
  6. 6.
    Lester, H.: A Survey of Hierarchical Non-linear Medical Image Registration. Pattern Recognition 32(1), 129–149 (1999)CrossRefGoogle Scholar
  7. 7.
    Hajnal, J., Hawkes, D., Hill, D.: Medical Image Registration. CRC Press, Boca Raton (2001)CrossRefGoogle Scholar
  8. 8.
    Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid Registration Using Free-form Deformations: Applications to Breast MR Images. IEEE T-MI 18(8), 712–721 (1999)Google Scholar
  9. 9.
    Schnabel, J., et al.: A General Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-form Deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, p. 573. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Chui, H., Rangarajan, A.: A New Point Matching Algorithm for Non-rigid Registration. CVIU 89(2-3), 114–141 (2003)zbMATHGoogle Scholar
  11. 11.
    Zheng, B., Takamatsu, J., Ikeuchi, K.: An Adaptive and Stable Method for Fitting Implicit Polynomial Curves and Surfaces. IEEE T-PAMI 32(3), 561–568 (2010)CrossRefGoogle Scholar
  12. 12.
    Bookstein, F.: Principal Warps: Thin-plate Splines and the Decomposition of Deformations. IEEE T-PAMI 11, 567–585 (1989)CrossRefzbMATHGoogle Scholar
  13. 13.
    Donato, G., Belongie, S.: Approximate Thin Plate Spline Mappings. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 21–31. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Medioni, G., Yasumoto, Y.: Corner Detection and Curve Representation Using Cubic B-Splines. In: Proc. IEEE ICRA (1986)Google Scholar
  15. 15.
    Rohr, K., et al.: Landmark-based Elastic Registration Using Approximating Thin-plate Splines. IEEE T-MI 20(6), 526–534 (2001)Google Scholar
  16. 16.
    Canny, J.: A Computational Approach to Edge Detection. IEEE T-PAMI 8(6), 679–698 (1986)CrossRefGoogle Scholar
  17. 17.
    Harris, C., et al.: A Combined Corner and Edge Detection. In: Proc. 4th AVC, pp. 147–151 (1988)Google Scholar
  18. 18.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  19. 19.
    Bay, H., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Matas, J., Chum, O., Urba, M., Pajdla, T.: Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. In: Proc. BMVC (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiang Xiang
    • 1
  1. 1.Key Lab of Intelligent Information Processing of CAS, Institute of Computing TechnologyChinese Academy of Sciences (CAS)BeijingChina

Personalised recommendations