Mixed-Integer Evolution Strategies and Their Application to Intravascular Ultrasound Image Analysis

  • Rui Li
  • Michael T. M. Emmerich
  • Ernst G. P. Bovenkamp
  • Jeroen Eggermont
  • Thomas Bäck
  • Jouke Dijkstra
  • Johan H. C. Reiber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

This paper discusses Mixed-Integer Evolution Strategies and their application to an automatic image analysis system for IntraVascular UltraSound (IVUS) images. Mixed-Integer Evolution Strategies can optimize different types of decision variables, including continuous, nominal discrete, and ordinal discrete values. The algorithm is first applied to a set of test problems with scalable ruggedness and dimensionality. The algorithm is then applied to the optimization of an IVUS image analysis system. The performance of this system depends on a large number of parameters that – so far – need to be chosen manually by a human expert. It will be shown that a mixed-integer evolution strategy algorithm can significantly improve these parameters compared to the manual settings by the human expert.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bovenkamp, E.G.P., Dijkstra, J., Bosch, J.G., Reiber, J.H.C.: Multi-agent segmentation of IVUS images. Pattern Recognition 37(4), 647–663 (2004)CrossRefGoogle Scholar
  2. 2.
    Emmerich, M., Schütz, M., Gross, B., Grötzner, M.: Mixed-Integer Evolution Strategy for Chemical Plant Optimization. In: Parmee, I.C. (ed.) Evolutionary Design and Manufacture (ACDM 2000), pp. 55–67. Springer, NY (2000)Google Scholar
  3. 3.
    Emmerich, M., Grötzner, M., Schütz, M.: Design of Graph-based Evolutionary Algorithms: A case study for Chemical Process Networks. Evolutionary Computation 9(3), 329–354 (2001)CrossRefGoogle Scholar
  4. 4.
    Hoffmeister, F., Sprave, J.: Problem independent handling of constraints by use of metric penalty functions. In: Fogel, L.J., Angeline, P.J., Bäck, T. (eds.) Evolutionary Programming V - Proc. Fifth Annual Conf. Evolutionary Programming (EP 1996), pp. 289–294. The MIT Press, Cambridge (1996)Google Scholar
  5. 5.
    Koning, G., Dijkstra, J., von Birgelen, C., Tuinenburg, J.C., Brunette, J., Tardif, J.-C., Oemrawsingh, P.W., Sieling, C., Melsa, S.: Advanced contour detection for threedimensional intracoronary ultrasound: validation – in vitro and in vivo. The International Journal of Cardiovascular Imaging (18), 235–248 (2002)Google Scholar
  6. 6.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990) Number ISBN 0-674-92101-1Google Scholar
  7. 7.
    Rudolph, G.: An Evolutionary Algorithm for Integer Programming. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 139–148. Springer, Heidelberg (1994)Google Scholar
  8. 8.
    Schwefel, H.-P.: Evolution and Optimum Seeking. Sixth Generation Computing Series. John Wiley, NY (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rui Li
    • 1
  • Michael T. M. Emmerich
    • 1
  • Ernst G. P. Bovenkamp
    • 2
  • Jeroen Eggermont
    • 2
  • Thomas Bäck
    • 1
  • Jouke Dijkstra
    • 2
  • Johan H. C. Reiber
    • 2
  1. 1.Natural Computing GroupLeiden UniversityLeidenThe Netherlands
  2. 2.Division of Image Processing, Department of Radiology C2SLeiden University Medical CenterLeidenThe Netherlands

Personalised recommendations