Medical Image Fusion by Combining Nonsubsampled Contourlet Transform and Sparse Representation

In this paper, we present a novel medical image fusion method by taking the complementary advantages of two powerful image representation theories: nonsubsampled contourlet transform (NSCT) and sparse representation (SR). In our fusion algorithm, the NSCT is firstly performed on each of the pre-registered source images to obtain the lowpass and high-pass coefficients. Then, the low-pass bands are merged with a SR-based fusion approach, and the high-pass bands are fused by employing the absolute values of coefficients as activity level measurement. Finally, the fused image is obtained by performing inverse NSCT on the merged coefficients. Several sets of medical source images with different combinations of modalities are used to test the effectiveness of the proposed method. Experimental results demonstrate that our method owns clear advantages over the fusion method based on NSCT or SR individually in terms of both visual quality and objective assessments.