Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 234-237.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Gaussian Process Assisted CMA-ES Application in Medical Image Registration

LOU Hao-feng1, ZHANG Duan2   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China1
    College of Computer Science &Technology,Zhejiang University of Technology,Hangzhou 310023,China2
  • Online:2019-02-26 Published:2019-02-26

Abstract: A gaussian process assisted covariance matrix adaptation evolution strategy(GPACMA-ES)optimization algorithm was proposed in this paper.The kernel function used in the GPACMA-ES algorithm is constructed by the cova-riance matrix.Taking advantage of the Gaussian process,which plays a key role in both online learning about the histo-ric experience and predicting the promising region which contains globally optimal solution,the frequency of calculating fitness function in the algorithm is reduced markedly.Meanwhile,in order to improve the efficiency of the algorithm,GPACMA-ES is sampling in the trust region.So it has rapid convergence and good global search capacity.Finally,a case study of medical image registration is examined to demonstrate the ability and applicability of the GPACMA-ES.Expe-riment results show that GPACMA-ES is proper for medical image registration than CMA-ES,and it has a better effect on the precision of registration while reducing the number of calculation of the fitness function.

Key words: Covariance matrix adaptation evolution strategy, Gaussian process, Medical image registration, Trust region

CLC Number: 

  • TP391
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