Mixed-integer optimization of coronary vessel image analysis using evolution strategies

ABSTRACT

In this paper we compare Mixed-Integer Evolution Strategies (MI-ES)and standard Evolution Strategies (ES)when applied to find optimal solutions for artificial test problems and medical image processing problems. MI-ES are special instantiations of standard ES that can solve optimization problems with different objective variable types (continuous, integer, and nominal discrete). Artificial test problems are generated with a mixed-integer test generator.The practical image processing problem iss the detection of the lumen boundary in IntraVascular UltraSound (IVUS)images. Based on the experimental results, it is shown that MI-ES generally perform better than standard ES on both artifical and practical image processing problems. Moreover it is shown that MI-ES can effectively improve the parameters settings for the IVUS lumen detection algorithm.

REFERENCES

• 1: E. G. P. Bovenkamp, J. Dijkstra, J. G. Bosch,and J.H.C. Reiber. Multi-agent segmentation of IVUS images. Pattern Recognition, 37(4):647--663, April 2004.
• 2: Thomas Bäck and Martin Schütz. Evolution Strategies for Mixed-Integer Optimization of Optical Multilayer Systems. Evolutionary Programming, 33--51, 1995.
• 3: L. Ballerini and L. Franzén. Genetic Optimization of Morphological Filters with Applications in Breast Cancer Detection, In Raidl et al. eds: Applications of Evolutionary Computing, pp.250--259, LNCS 3005, 2004,Springer.
• 4: S. Cagnoni, A. B. Dobrzenieki, R. Poli and J. C. Yanch: Genetic algorithm-based interactive segmentation of 3D medical images. Image and Vision Computing, 17(12): 881--895, October 1999.
• 5: K. Chen, I. C. Parmee and C. R. Gane. Dual mutation strategies for mixed-integer optimisation in power-station design: Evolutionary Computation, 1997., IEEE International Conference (1997),IEEE Press
• 6: M. Emmerich, M. Schütz, B. Gross and M. Grötzner. Mixed-Integer Evolution Strategy for Chemical Plant Optimization. In I.C. Parmee, editor, Evolutionary Design and Manufacture (ACDM 2000), 2000, pp. 55--67, Springer NY.
• 7: F. Hoffmeister and J. Sprave. Problem independent handling of constraints by use of metric penalty functions. In L.J. Fogel, P.J. Angeline, and Th. Bäck,eds.: Evolutionary Programming V -Proc. Fifth Annual Conf. Evolutionary Programming (EP '96), pp.289--294. The MIT Press, 1996.
• 8: G. Koning, J. Dijkstra, C. von Birgelen, J. C. Tuinenburg, J. Brunette, J-C. Tardif, P. W. Oemrawsingh, C. Sieling, and S. Melsa. Advanced contour detection for three-dimensional intracoronary ultrasound: validation in vitro and in vivo. The International Journal of Cardiovascular Imaging ,(18):235--248, 2002.
• 9: Allen Newell, Unified theories of cognition, Harvard University Press, Cambridge, MA, 1990
• 10: Günter Rudolph, An Evolutionary Algorithm for Integer Programming, Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature, p.139-148, October 09-14, 1994
• 11: Hans-Paul Paul Schwefel, Evolution and Optimum Seeking: The Sixth Generation, John Wiley & Sons, Inc., New York, NY, 1993

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization

General Terms:
Algorithms, Design, Experimentation

Keywords:
IntraVascular ultraSound (IVUS), evolution strategies (ES), mixed-Integer evolution strategies (MI-ES)