Because of a different imaging mechanism

different modality medical images provide a variety of characteristics about image quality

space

and non-overlay complementary information. In clinical use

we need to analyze the result of multimodal medical images. In order to use medical images effectively and reasonably

a medical image fusion algorithm is proposed

combining the advantages of multi-scale and multiple directions in the Contourlet transformation. First

multi-scale and multiple directions decomposition coefficients are obtained through Contourlet transformation. Second

fusion rules are proposed by analyzing the characteristics of Contourlet transformation coefficients. An optimized image fusion rule is proposed in low frequency sub-band coefficients and high frequency sub-band coefficients. For low frequency sub-band coefficients

the weighted regional variance fusion rule is adopted in view of the image detail characteristics. The high frequency sub-band coefficients are fused by a condition-weighted rule of the main image in view of the edge detail characteristics. Finally

the final fusion image is acquired through the Contourlet inverse transformation. Different fusion rules based on Contourlet transformation and different fusion methods are analyzed. The fusion results are analyzed and compared with the measurement of human visual system and objective evaluation. Compare the new fusion method with other classical fusion algorithm to confirm the advantages of the new method. The experimental results show that the proposed algorithm is effective in retaining the original images' information and reserving the edge features successfully. A medical image weighting fusion algorithm is proposed based on Contourlet transformation. Medical images

including CT and MRI

are used for the experiments. The results show that the complementary information of medical image can be highlighted and the image definition has improved significantly.