Аннотация:In this paper, we propose an improved acceleration
scheme for the mutual entropy maximization method for
biomedical image registration. Our approach is based on fast
adaptive bidirectional empirical mode decomposition (FABEMD)
and aims to reduce the computational complexity of the mutual
entropy maximization algorithm by extracting only information
essential for registration. We apply several adaptive optimization
techniques in a row: image structural reduction using FABEMD,
histogram sparsification, image downsampling, and multilevel
parametric space search. Our experiments show that with the
proposed scheme registration is performed up to 150 times faster
without noticeable loss of accuracy for typical MRI images.