Abstract: Aiming at the problem that singlemodality medical images could not provide comprehensive and complementary information for clinical diagnosis, the author proposed a multimodal medical image fusion method based on nonsubsampled shearlet transform (NSST) and improved sparse representation (ISR). Firstly, the NSST tool was used to decompose the source image into a low frequency subband and several high frequency subbands. Secondly, the ISR method was used to fuse the lowfrequency subbands, and the details of the lowfrequency subbands were removed by the sobel operator and the guided filter, thereby improving the fusion efficiency of the lowfrequency subbands. At the same time, the highfrequency subbands were fused by using the maximum absolute value fusion rule. Finally, the final fused image was obtained by the inverse NSST transform of the fused lowfrequency and highfrequency subbands. Experimental results show that proposed method is superior to other fusion methods in subjective visual performance and objective evaluation.

Key words: medical image fusion, nonsubsampled shearlet, improved sparse representation, fusion rule