A novel fusion algorithm for medical images using HMT model in aliasing-free contourlet transform (AFCT) domain is presented. First

the frequency aliasing of the original contourlet transform is investigated

and we make sure that the main reason of aliasing is the two lowpass filters of laplacian pyramid (LP) do not satisfy the nyquist-shannon sampling theorem. Then

instead of using LP

a new multiscale decomposition using two channel filter banks by considering a band limiting constraint on the low-pass filter is designed; and combined with directional filter banks

the AFCT is realized. On this basis

a medical image formation model using hidden Markov tree (HMT) to capture the correlations between the coefficients across decomposition scales is proposed. Finally

based on this image formation model

the expectation-maximization (EM) algorithm is used to estimate the model parameters and produce the fused image. The fusion experi〖HJ〗ments have been made on CT/MR and MR-T1/MR-T2

comparing with the traditional fusion methods which is based on wavelet transform and contourlet transform

the proposed algorithm can provide a more satisfactory outcome in terms of visual quality and quantitative criterion.