为了更好地对医学图像进行融合，提出了一种利用抗混叠轮廓波HMT模型的图像融合新算法。该算法首先对原始轮廓波变换的频谱混叠问题展开研究，明确LP分解中的两个低通滤波器不满足Nyquist抽样定律是造成混叠的主要原因。接着，在对低通滤波器考虑带限约束条件下，设计了一种能抑制混叠的利用双通道滤波器组结构的多尺度分解方案，用于代替原始轮廓波变换的LP分解，结合方向滤波器组，实现了一种抗混叠的轮廓波变换。在此基础上，提出一种采用隐马尔可夫树（HMT）来刻划变换系数尺度间相关性的医学图像成像模型，并以期望最大化算法估

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.