EGGDD: An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain

Abstract

Most of the traditional medical image fusion methods that use the multi-scale decomposition schemes suffer from the bad image representations and the loss of the dependency in different highpass subbands. To deal with these problems, a novel dependency model, named Explicit Generalized Gaussian Density Dependency (EGGDD) model, is developed by the shift-invariant shearlet transform (SIST). Substantially different from describing the dependency by two hidden states in the Hidden Markov Tree (HMT) model, we provide the scheme to explicitly describe the marginal statistics of each highpass subband using the Generalized Gaussian Density (GGD), as well as the dependency between different subbands by the Kullback–Leibler distance (KLD). After embedding the obtained dependency into each highpass subband, an efficient fusion scheme, inspired by the divisive normalization response in the V1 visual cortex model, is developed to combine the highpass-subband coefficients. The experiments demonstrate that the developed method can produce better fusion results than those of some existing methods by the comparison of visual sense and quantitative measurements in terms of mutual information, entropy, spatial frequency, standard deviation, QAB/F and QW.