Medical image fusion using optimal feature selection methods based on second generation contourlet transform

摘要

As a novel of multi-resolution analysis tool, second generation contourlet transform (SGCT) provides flexible multiresolution, anisotropy, and directional expansion for medical imaging systems. In this paper, a novel fusion method for multimodal medical images based on SGCT is proposed. Firstly, we utilise the SGCT to decompose the multimodal medical images with highpass subbands and lowpass subbands. Then, for the highpass subbands, the weighted sum modified Laplacian (WSML) method is utilised to generate the high frequency coefficients to recovery image details. For the lowpass subbands, the maximum local energy (MLE) method is combined with 'local patch' idea for low frequency coefficients selection. Finally, the fused image is obtained by applying inverse SGCT to combine lowpass and highpass subbands. During abundant experiments, we evaluate the proposed method both human visual and quantitative analysis. Compare with the-state-of-the-art methods, the new strategy for attaining image fusion with satisfactory performance.