Mixed-Integer evolution strategies and their application to intravascular ultrasound image analysis

  • Authors:

  • Rui Li;Michael T. M. Emmerich;Ernst G. P. Bovenkamp;Jeroen Eggermont;Thomas Bäck;Jouke Dijkstra;Johan H. C. Reiber

  • Affiliations:

  • Natural Computing Group, Leiden University, Leiden, The Netherlands;Natural Computing Group, Leiden University, Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, Leiden, The Netherlands;Natural Computing Group, Leiden University, Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, Leiden, The Netherlands

  • Venue:
  • EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
  • Year:
  • 2006

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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.