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In order to overcome the problem that many medical image fusion methods ignore the correlation degree of different image energy when they use the energy information of image to fuse images, which makes the fusion results have the phenomenon of loss of detail and ambiguity, a medical image fusion algorithm based on non subsampled shearlet transform coupled with energy correlation degree is proposed. With the help of NSST transform, the input medical image is analyzed in multi-scale to obtain its low-frequency and high-frequency subband coefficients. Based on the energy information of the image, the energy correlation function is constructed to measure the correlation degree of different images. According to the correlation degree of different images, different fusion rules of low-frequency subbands are designed to obtain fusion low-frequency subbands with rich information and good coherence. On the basis of spatial frequency function, the diagonal information of the image is injected to make it a multivariate spatial frequency function to calculate the clarity of the image. The standard deviation function is introduced to calculate the contrast of the image. Combined with the clarity and contrast information of the image, the fusion high-frequency subband with better texture and contrast characteristics is obtained. Based on the inverse NSST transform, the fusion result is reconstructed. Subjective and objective experimental results show that compared with the current popular medical image fusion technology, the proposed method has higher fusion quality, more texture details and higher definition.