Advertisement

On Genetic Algorithms Optimization for Heart Motion Compensation

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

Heart motion compensation is a challenging problem within medical robotics and it is still considered an open research area due to the lack of robustness. As it can be formulated as an energy minimization problem, an optimization technique is needed. The selection of an adequate method has a significant impact over the global solution. For this reason, a new methodology is presented here for solving heart motion compensation in which the central topic is oriented to increase robustness with the goal of achieving a balance between efficiency and efficacy. Particularly, genetic algorithms are used as optimization technique since they can be adapted to any real application, complex and oriented to work in real-time problems.

Keywords

Genetic Algorithms Deformation Stochastic Optimization Beating Heart Surgery Robotic Assisted Surgery 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Livesay, J.J.: The benefits of off-pump coronary bypass: A reality or an illusion? Texas Heart Institute Journal (2003)Google Scholar
  2. 2.
    Karamanoukian, H.L.: Decreased incidence of postoperative stroke following on-pump coronary artery bypass. Journal of the American College of Cardiology (2002)Google Scholar
  3. 3.
    World Health Organization (2011), http://www.who.int/en/
  4. 4.
    Lemma, A., Mangini, A., Redaelli, A., Acocella, F.: Do cardiac stabilizers really stabilize? experimental quantitative analysis of mechanical stabilization. Interactive CardioVascular and Thoracic Surgery (2005)Google Scholar
  5. 5.
    Jacobs, S., Holzhey, D., Mohr, F., Falk, V.: Limitations for manual and telemanipulator-assisted motion tracking and dexterity for endoscopic surgery. In: Proc. Int. Congr. Comput. Assisted Radiol. Surg., pp. 673–677 (2003)Google Scholar
  6. 6.
    Salvendy, G.: Manual control and tracking. In: Handbook of Human Factors, pp. 182–218. Wiley, New York (1987)Google Scholar
  7. 7.
    Nakamura, Y., Kishi, K., Kawakami, H.: Heartbeat Synchronization for Robotic Cardiac Surgery. In: International Conference on Robotics and Automation, Seoul, Korea, pp. 2014–2019 (2001)Google Scholar
  8. 8.
    Ortmaier, T., Groger, M., Boehm, D.H., Falk, V., Hirzingerw, G.: Motion estimation in beating heart surgery. IEEE Trans. Biomed. Eng., 1729–1740 (2005)Google Scholar
  9. 9.
    Noce, A., Triboulet, J., Poignet, P.: Efficient tracking of the heart using textures. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS (2007)Google Scholar
  10. 10.
    Lo, B., Chung, A.J., Stoyanov, D., Mylonas, G., Yang, G.Z.: Real-time intra-operative 3D tissue deformation recovery. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 1387–1390 (2008)Google Scholar
  11. 11.
    Richa, R., Poignet, P., Liu, C.: Three-dimensional motion tracking for beating heart surgery using a thin-plate spline deformable model. The International Journal of Robotics Research (2009)Google Scholar
  12. 12.
    Hadamard, J.: Lectures on the Cauchy Problems in Linear Partial Differential Equations. Yale University Press, New Haven (1923)Google Scholar
  13. 13.
    Aviles, A.I., Casals, A.: Interpolation Based Deformation Model for Minimally Invasive Beating Heart Surgery. In: Roa Romero, L.M. (ed.) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. IFMBE Proceedings, vol. 41, pp. 372–375. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    Lewis, B., Reichel, L.: Arnoldi-Tikhonov regularization methods, pp. 1–21. Elsevier Science (2008)Google Scholar
  15. 15.
    Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42, 577–685 (1989)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Rudin, L., Osher, S.J., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)CrossRefMATHGoogle Scholar
  17. 17.
    Rouet, J.M., Jacq, J.J., Roux, C.: Genetic algorithms for a robust 3-D MR-CT registration. IEEE T. Inf. Technol. B. 4(2), 126–136 (2000)CrossRefGoogle Scholar
  18. 18.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press (1975)Google Scholar
  19. 19.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addision-Westley (1989)Google Scholar
  20. 20.
    Mitchell, M.: An introduction to genetic algorithms. The MIT Press (1998)Google Scholar
  21. 21.
    Sivanandam, S.N., Deepa, S.N.: An introduction to genetic algorithms. Springer (2008)Google Scholar
  22. 22.
    Stoyanov, D., Mylonas, G., Deligianni, F., Darzi, A., Yang, G.Z.: Soft-tissue Motion Tracking and Structure Estimation for Robotic Assisted MIS Procedure. Medical Image Computing and Computer Assisted Interventions 2, 139–146 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Intelligent Robotics and Systems GroupThe Polytechnic University of Catalonia-BarcelonaTechBarcelonaSpain
  2. 2.Institute for Bioengineering of CataloniaRobotics LabBarcelonaSpain

Personalised recommendations