Optimizing a Medical Image Analysis System Using Mixed-Integer Evolution Strategies

  • Rui Li
  • Michael T. M. Emmerich
  • Jeroen Eggermont
  • Ernst G. P. Bovenkamp
  • Thomas Bäck
  • Jouke Dijkstra
  • Johan H. C. Reiber
Part of the Studies in Computational Intelligence book series (SCI, volume 213)


We will discuss Mixed-Integer Evolution Strategies (MIES) and their application to the optimization of control parameters of a semi-automatic image analysis system for Intravascular Ultrasound (IVUS) images. IVUS is a technique used to obtain real-time high-resolution tomographic images from the inside of coronary vessels and other arteries. The IVUS image feature detectors used in the analysis system are expert-designed and the default parameters are calibrated manually so far. The new approach, based on MIES, can automatically find good parameterizations for sets of images, which provide in better results than manually tuned parameters. From the algorithmic point of view, the difficulty is designing a blackbox optimization strategy that can deal with nonlinear functions and different types of parameters, including integer, nominal discrete and continuous variables. MIES turns out to be well suited for this task. The results presented in this contribution will summarize and extend recent studies on benchmark functions and the IVUS image analysis optimization problem.


Intravascular Ultrasound (IVUS) Evolution Strategies (ES) Mixed- Integer Evolution Strategies (MIES) Coronary Artery Disease (CAD) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rui Li
    • 1
  • Michael T. M. Emmerich
    • 1
  • Jeroen Eggermont
    • 2
  • Ernst G. P. Bovenkamp
    • 2
  • Thomas Bäck
    • 1
  • Jouke Dijkstra
    • 2
  • Johan H. C. Reiber
    • 2
  1. 1.Natural Computing Group, Leiden Institute of Advanced Computer ScienceLeiden UniversityThe Netherlands
  2. 2.Division of Image Processing, Department of Radiology C2SLeiden University Medical CenterThe Netherlands

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