Fast medical image registration using bidirectional empirical mode decomposition

This paper focuses on an acceleration of the mutual information maximization method for medical image registration. Our approach is based on fast adaptive bidirectional empirical mode decomposition (FABEMD). The registration is performed for the informative intrinsic image modes. It aims to reduce the computational complexity of the mutual entropy maximization algorithm by extracting only essential data. Optimization process consists of several steps: image structural reduction using FABEMD, sequential parameters search, image downsampling, and, finally, multilevel parametric space search. We compare our approach to standard mutual information maximization method (MMI) and analyze results for multimodal medical images. Experiments show that proposed method produces consistent results very close to MMI, while reducing the registration time by 200 time on average. •A new fast medical image registering algorithm is proposed.•The algorithm is an optimization of the mutual entropy maximization based method.•The optimization uses fast adaptive bidirectional empirical mode decomposition.•The method reduces the computational complexity of the mutual entropy maximization.