Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis

Authors:
Rui Li;Jeroen Eggermont;Michael T. Emmerich;Ernst G. Bovenkamp;Thomas Bäck;Jouke Dijkstra;Johan H. Reiber
Affiliations:
Natural Computing Group, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands;Natural Computing Group, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands;Natural Computing Group, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands;Division of Image Processing, Department of Radiology C2S, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
Venue:
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Year:
2009

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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 setof 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.