目的 医学超声图像中的斑点噪声降低了图像质量并且限制了超声图像自动化诊断技术的发展。针对斑点噪声问题，提出一种新型的基于小波和双边滤波的去噪算法。方法 首先，根据医学超声图像在小波域内的统计特性，在通用小波阈值函数的基础之上，改进了小波阈值函数。其次，将无噪信号的小波系数和斑点噪声的小波系数分别建模为广义拉普拉斯分布模型和高斯分布模型，利用贝叶斯最大后验估计方法得到了新型的小波收缩算法，利用小波阈值法对小波域内的高频信号分量进行去噪。最后，对小波域内的低频信号分量进行双边滤波处理，然后利用小波逆变换便得到去噪后的图像。结果 在仿真实验中，通过与其他7种去噪算法作对比，观察峰值信噪比（PSNR）等图像质量评价指标，结果表明本文算法的去噪效果优于其他相关算法。临床超声图像的实验结果进一步验证了本文算法的去噪性能。结论 实验表明本文算法能够很好地抑制斑点噪声，并且能保留图像病灶边缘等细节。

Objective Speckle noise is a granular structure, and it occurs when a coherent source and a non-coherent detector are used to interrogate a medium. Speckle noise is an undesirable part in the ultrasound image, since it can mask the small difference in grey level and degrade the image quality. The task of despeckling is an important step for analysis and processing of ultrasound images, which is essential for automatic diagnostic techniques. The wide spread of mobile and portable ultrasound scanning instruments also necessitates that a clearer image must be obtained to the medical practitioner. A novel despeckling algorithm for medical ultrasound images is proposed, which is based on the wavelet transformation and a bilateral filter. Method According to the statistical properties of medical ultrasound images in wavelet domain, an improved wavelet threshold function is proposed on the basis of the universal wavelet threshold function. The proposed wavelet threshold function is obtained by multiplying the universal wavelet threshold function with an adjustable parameter. The noise-free signal and speckle noise in the wavelet domain are modeled as generalized Laplace distribution and Gaussian distribution respectively. The Bayesian maximum a posteriori estimation is applied to get a new wavelet shrinkage algorithm. The speckle noise in the high-pass component in wavelet domain of ultrasound images is suppressed by the new wavelet shrinkage algorithm. The speckle noise in the low-pass approximation component is filtered by the bilateral filter, since the low-pass approximation component of ultrasound images also contains some speckle noise. The filtered image is then obtained via inverse wavelet transform. Result The comparative experiments with seven other despeckling methods are conducted. Several image quality metrics are used to compare the performance of speckle reduction, such as the peak signal to noise ratio (PSNR), the structure similarity (SSIM) and Pratt's figure of merit (FoM), as well as the computational time of different methods is presented. The filtered images of the proposed algorithm get the best result by compared to the PSNR and FoM values with other seven despeckling methods. The best result of the PSNR and FoM value means that the proposed algorithm can suppress more speckle noise and the filtered image has the similar edge to witch of the noise-reference synthetic image. In the comparison of SSIM values, the proposed algorithm also gets good performance, which means that the proposed algorithm can retain a structure similar structure to the noise-free reference synthetic ultrasound image. Observing the computational time, the proposed algorithm does not have superiority in the aspect of time consuming. The experiment of clinical ultrasound breast images with lesions is also conducted, and we can find that the proposed algorithm gets a pretty good despeckling performance. Conclusion Since speckle noise limits the development of automatic diagnostic technology for ultrasound images, we propose an improved despeckling algorithm on the basis of the wavelet transform and the bilateral filter. The experiments of synthetic ultrasound images and clinical ultrasound breast images show that the proposed despeckling algorithm not only has better speckle reduction than the other seven filters, but also can preserve image details such as the edge of lesions.