Aiming at the problem that the detailed texture is not clear enough for the fused medical image, this study proposes a new medical image fusion algorithm on the basis of non-subsampled shearlet transform (NSST) to fuse the multimodal medical image to enhance the detail structure extraction, improve fused image quality and provide a basis for medical diagnosis. First of all, the registered source image is decomposed by NSST to obtain a low-frequency sub-band and a series of high-frequency sub-band. Then, for the low-frequency sub-band coefficients, this study proposes a fusion method using sub-band selection between the regional average energy and regional standard deviation. For high-frequency sub-band coefficients, the fusion method is performed using the new sum of modified Laplacian (NSML). Afterwards, the fused low-frequency, high-frequency sub-band coefficients are inversely transformed by NSST to obtain a fused image. Finally, a large number of experiments were performed on grayscale and color medical multimodal images, and IE, SF, SD, and AG were selected to evaluate the fused images. The simulation results show that the proposed algorithmimprove subjective visual effect and objective evaluation. Compared with other algorithms, the average values of IE, SD, SF, and AG increased by 2.99%, 4.06%, 1.78% and 1.37%, respectively. The fused image contains more detailed texture information and better visual effect.