关键词: 医学图像融合, K奇异值分解, 系数重用正交匹配追踪, 稀疏表示, 字典训练

Abstract: The dictionary training process is time-consuming, and it is difficult to obtain accurate sparse representation by using a single dictionary to express brain medical images currently, which leads to the inefficiency of image fusion. In order to solve the problems, a Computed Tomography (CT)/Magnetic Resonance (MR) brain image fusion method via improved coupled dictionary learning was proposed. Firstly, the CT and MR images were regarded as the training set, and the coupled CT and MR dictionary were obtained through joint dictionary training based on improved K-means-based Singular Value Decomposition (K-SVD) algorithm respectively. The atoms in CT and MR dictionary were regarded as the features of training images, and the feature indicators of the dictionary atoms were calculated by the information entropy. Then, the atoms with the smaller difference feature indicators were regarded as the common features, the rest of the atoms were considered as the innovative features. A fusion dictionary was obtained by using the rule of "mean" and "choose-max" to fuse the common features and innovative features of the CT and MR dictionary separately. Further more, the registered source images were compiled into column vectors and subtracted the mean value. The accurate sparse representation coefficients were computed by the Coefficient Reuse Orthogonal Matching Pursuit (CoefROMP) algorithm under the effect of the fusion dictionary, the sparse representation coefficients and mean vector were fused by the rule of "2-norm max" and "weighted average" separately. Finally, the fusion image was obtained via reconstruction. The experimental results show that, compared with three methods based on multi-scale transform and three methods based on sparse representation, the image visual quality fused by the proposed method outperforms on the brightness, sharpness and contrast, the mean value of the objective parameters such as mutual information, the gradient based, the phase congruency based and the universal image quality indexes under three groups of experimental conditions are 4.1133, 0.7131, 0.4636 and 0.7625 respectively, the average time in the dictionary learning phase under 10 experimental conditions is 5.96 min. The proposed method can be used for clinical diagnosis and assistant treatment.

Key words: medical image fusion, K-means-based Singular Value Decomposition (K-SVD), Coefficient Reuse Orthogonal Matching Pursuit (CoefROMP), sparse representation, dictionary training