Advertisement

Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis

  • Rui Li
  • Jeroen Eggermont
  • Michael T. M. Emmerich
  • Ernst G. P. Bovenkamp
  • Thomas Bäck
  • Jouke Dijkstra
  • Johan H. C. Reiber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4448)

Abstract

This paper discusses a study towards dynamic fitness based partitioning in IntraVascular UltraSound (IVUS) image analysis. Mixed-Integer Evolution Strategies (MI-ES) have recently been successfully used to optimize control parameters of a multi-agent image interpretation system for IVUS images lumen detection. However, because of complex interpretation contexts, it is impossible to find one single solution which works well on each possible image of each possible patient. Therefore it would be wise to let MI-ES find a set of solutions based on an optimal partition of IVUS images. Here a methodology is presented which does dynamic fitness based partitioning of the data during the MI-ES parameter optimization procedure. As a first step we applied this method to a challenging artificial test case which demonstrates the feasibility of our approach.

Keywords

Problem Instance IntraVascular UltraSound Parameter Solution IVUS Image Evolution Strategy 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bovenkamp, E.G.P., Eggermont, J., Li, R., Emmerich, M.T.M., Bäck, Th., Dijkstra, J., Reiber, J.H.C.: Optimizing IVUS Lumen Segmentations using Evolutionary Algorithms. In: Medical Image Computing and Computer-Assisted Intervention, Kopenhagen, Denmark (2006)Google Scholar
  2. 2.
    Handl, J., Knowles, J.: An investigation of representations and operators for evolutionary data clustering with a variable number of clusters. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) Parallel Problem Solving from Nature - PPSN IX. LNCS, vol. 4193, pp. 839–849. Springer, Berlin Heidelberg New York (2006)CrossRefGoogle Scholar
  3. 3.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  4. 4.
    Keijzer, M., Merelo, J.J., Romero, G., Schoenauer, M.: Evolving objects: a general purpose evolutionary computation library. In: EA-01, Evolution Artificielle, 5th International Conference in Evolutionary Algorithms (2001)Google Scholar
  5. 5.
    Li, R., Emmerich, M.T.M., Eggermont, J., Bovenkamp, E.G.P., Bäck, Th., Dijkstra, J., Reiber, J.H.C.: Mixed-integer optimization of coronary vessel image analysis using evolution strategies. In: Genetic and Evolutionary Computation Conference, GECCO, Proceedings, pp. 1645–1652 (2006)Google Scholar
  6. 6.
    Vanneschi, L., Mauri, G., Valsecchi, A., Cagnoni, S.: Heterogeneous cooperative coevolution: strategies of integration between gp and ga. In: Genetic and Evolutionary Computation Conference, GECCO, Proceedings, pp. 361–368 (2006)Google Scholar
  7. 7.
    Roberts, M.E., Claridge, E.: Cooperative coevolution of image feature construction and object detection. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VIII. LNCS, vol. 3242, pp. 902–911. Springer, Berlin Heidelberg New York (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rui Li
    • 1
  • Jeroen Eggermont
    • 2
  • Michael T. M. Emmerich
    • 1
  • Ernst G. P. Bovenkamp
    • 2
  • Thomas Bäck
    • 1
  • Jouke Dijkstra
    • 2
  • Johan H. C. Reiber
    • 2
  1. 1.Natural Computing Group, Leiden University, P.O. Box 9512, 2300 RA LeidenThe Netherlands
  2. 2.Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, P.O. Box 9600, 2300 RC LeidenThe Netherlands

Personalised recommendations