Information of the single-modality medical image is limited;thus

it cannot reflect all the details of relevant organizations and may cause misdiagnosis in the clinical setting.A scientific and effective fusion algorithm is proposed to fuse multimodal medical images

enrich fusion image information

and improve image quality to provide the basis for clinical diagnosis and solve previously mentioned problem. A medical image fusion algorithm based on non-subsampled shearlet transform (NSST) is proposed.First

low-and high-frequency sub-bands are obtained using NSST.Then

on the basis of the low-frequency sub-band image feature

a fusion rule based on low-frequency coefficients combined with pulse-coupled neural network is adopted for low-frequency sub-band images.On the basis of the different structural similarities (SSIM) of high-frequency sub-band images

the fusion rule of combined visual sensitivity coefficient (VSC) with improved gradient energy is adopted for low SSIM sub-bands

whereas the fusion rule of combined VSC with regional energy is applied for high SSIM sub-bands.Furthermore

a closed-loop feedback is introduced into the fusion rule to optimize variables adaptively using the sum of the SSIM and edge-based similarity measure() as objective evaluation.The image is restructured by inverse NSST. Experiments are conducted on gray and colored images and compared with four other types of fusion methods in terms of subjective visual effect and objective evaluation criteria.This method exhibits a good fusion effect.The factors and evaluation criteria of edge difference are the best

whereas other indicators are better.Compared with the multi-modality medical image fusion method based on non-subsampled contourlet transform by Jin zhenyi

five groups of spatial frequencies were increased by 11.8%

24.7%

83.4%

11.9%

and 30.3%;edge-based similarity measures were increased by 6.7%

15%

40%

50%

and 12%;SSIM were increased by 0.7%

7.3%

2

4%

-3.6%

and 2.1%;and cross-entropy measures were decreased by 16.9%

1.6%

-27.4%

6.1%

and 0.4%. The proposed algorithm can effectively improve the quality of multimodal medical image fusion and increase the complementary information among different modalities.This algorithm is superior to existing medical image fusion algorithms.The fused image has more grand character and equally abundant and more in accord with human vision character.