Advertisement

Online Parameter Estimation for Surgical Needle Steering Model

  • Kai Guo Yan
  • Tarun Podder
  • Di Xiao
  • Yan Yu
  • Tien-I Liu
  • Keck Voon Ling
  • Wan Sing Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

Estimation of the system parameters, given noisy input/output data, is a major field in control and signal processing. Many different estimation methods have been proposed in recent years. Among various methods, Extended Kalman Filtering (EKF) is very useful for estimating the parameters of a nonlinear and time-varying system. Moreover, it can remove the effects of noises to achieve significantly improved results. Our task here is to estimate the coefficients in a spring-beam-damper needle steering model. This kind of spring-damper model has been adopted by many researchers in studying the tissue deformation. One difficulty in using such model is to estimate the spring and damper coefficients. Here, we proposed an online parameter estimator using EKF to solve this problem. The detailed design is presented in this paper. Computer simulations and physical experiments have revealed that the simulator can estimate the parameters accurately with fast convergent speed and improve the model efficacy.

Keywords

Online parameter estimation spring/damper model EKF 

References

  1. 1.
    Loser, M., Navab, N.: A New Robotic System for Visually Controlled Percutaneous Interventions under CT. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 887–896. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Stoianovici, D., Cadeddu, J.A., Demaree, R.D., Basile, H.A., et al.: An Efficient Needle Injection Technique and Radiological Guidance Method for Percutaneous Procedures. LNCS, vol. 1205. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Kaiguo, Y., Ng, W.S., Yu, Y., Podder, T., Liu, T.-I., Cheng, C.W.S., Ling, K.V.: Needle Steering Modeling and Analysis using Unconstrained Modal Analysis. In: BIOROB, Italy (2006)Google Scholar
  4. 4.
    Terzopoulos, D., Waters, K.: Analysis and Synthesis of Facial Image Sequences using Physical and Anatomical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (1993)Google Scholar
  5. 5.
    Boux de Casson, F., Laugier, C.: Modeling the Dynamics of a Human Liver for a Minimally Invasive Surgery Simulator. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 1156–1165. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Webster, R.: Elastically Deformable 3D Organs for Haptic Surgical Simulation. In: Proceedings of Medicine Meets Virtual Reality, Newport Beach (2002)Google Scholar
  7. 7.
    Neumann, P.F., Sadler, L.L., Gieser, J.: Virtual Reality Vitrectomy Simulator. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, Springer, Heidelberg (1998)Google Scholar
  8. 8.
    de Wit, C.C., Siciliano, B., Bastin, G.: Theory of Robot Control. Springer, New York (1996)zbMATHGoogle Scholar
  9. 9.
    Morf, M., Kailath, T.: Square-Root Algorithms for Least-Squares Estimation. Transaction on Automatic Control AC-20 (1975)Google Scholar
  10. 10.
    Lu, M., Qiao, X.: Parallel Computation of the Modified Extended Kalman Filter. International Journal of Computer Math. 45 (1992)Google Scholar
  11. 11.
    Abiko, S., Yoshida, K.: On-line Parameter Identification of a Payload Handeled by Flexible Based Manimulator. In: Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Japan (2004)Google Scholar
  12. 12.
    Chia, T.L., Chow, P.-C., Chizeck, H.J.: Recursive Parameter Identification of Constrained Systems: An Application to Electrically Stimulated Muscle. IEEE Transactions on Biomedical Engineering 38 (1991)Google Scholar
  13. 13.
    Kumagai, A., Liu, T.-I., Holzian, P.: Control of Shape Memory Alloy Actuators with A Neuro-Fuzzy Feedforward Model Element. Journal of Intelligent Manufacturing 17, 45–56 (2006)CrossRefGoogle Scholar
  14. 14.
    Zarchan, P., Musoff, H.: Fundamentals of Kalman Filtering, A Practical Approach, the American Institute of Aeronautics and Astronautics. Inc.1801 Alexander Bell Drive, Reston, Virginia 190, 4344–20191 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kai Guo Yan
    • 1
  • Tarun Podder
    • 3
  • Di Xiao
    • 1
  • Yan Yu
    • 3
  • Tien-I Liu
    • 4
  • Keck Voon Ling
    • 2
  • Wan Sing Ng
    • 1
  1. 1.Schools of MAENanyang Technological UniversitySingapore
  2. 2.Schools of EEENanyang Technological UniversitySingapore
  3. 3.Department of Radiation OncologyUniversity of RochesterU.S.A.
  4. 4.Computer Integrated Manufacturing LabCalifornia State UniversitySacramentoU.S.A.

Personalised recommendations