提出了一种新的医学超声图象去噪方法.首先，原始超声图象经对数变换，其乘性散粒噪声变为了加性噪声 ;然后再经小波变换后，基于隐马尔可夫树模型，应用贝叶斯方法去除加性噪声 ;最后，经小波反变换和指数变换恢复去噪后的原始超声图象.测试结果表明，此方法在有效去除噪声的同时，能保留原始图象的细节边缘.针对超声图象还对几种去噪算法作出定性比较，并对去噪性能给出定量分析，实验结果表明，该方法是可行的

One of the hot key in medical image processing is how to suppressing speckle noise in ultrasound image. The low quality of ultrasound imaging brings some difficulty to sequential image processing and image analysis due to the effect of its inherent speckle noise. In this paper, a speckle suppression method for medical ultrasound image is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. Then, the wavelet domain multiscale representation of image is regarded as Hidden Markov Tree model. The model is trained by the efficient EM algorithm,which is called Baum weltch algorithm. Speckle noise of ultrasound image is reduced by Bayesian estimator based on Hidden Markov model. Finally, the invert discrete wavelet transform and the exponent transform of the estimated wavelet coefficients obtain the denoised image in turn. Performance of the proposed method has been tested on ultrasound image. The results show the method effectively reduces the speckle while preserving the edges of the original image. Current state of the art methods, such as soft and hard thresholding, are applied on actual ultrasound medical images and compared with the novel method. The achieved performance improvement is quantified. The experiment results show the proposed method is feasible and reasonable.