Medical image fusion is important in the field of disease diagnosis because it can improve the availability of information contained in images. To address the problem of multi-modal medical image fusion

this study proposes a new algorithm for medical image fusion based on the characteristics of lifting wavelet transform. First

the source multi-modal medical images after registration are decomposed into low and high frequency sub-bands by applying lifting wavelet transform. Second

image fusion rules are put forward according to the different features of the low and high frequency sub-bands. A fusion rule based on weighted region average energy is adopted for the low-frequency sub-band coefficients. For the high-frequency sub-band coefficients

the weighed box-counting method is applied in the fusion rules of low-rise sub-bands with low noise content

and the fusion rule of the weighed local area energy of the image gradient is used for high-rise sub-bands with high noise content. Several experiments that compare the previous with new medical image fusion algorithms are conducted for gray and color images. The experiment results are then analyzed in terms of visual quality and objective evaluation. The proposed algorithm can effectively preserve edge information. This study demonstrates that the proposed algorithm based on lifting wavelet transform can effectively preserve a large amount of information and significantly improve the performance of fusion images in terms of visual quality and objective evaluation index.