贝叶斯改进阈值超声图像去噪方法

Denoising ultrasound imagings based on an improved BayesShrink threshold method

针对超声图像散斑噪声,提出一种贝叶斯改进阈值超声图像去噪方法。超声图像质量下降的主要原因是乘性噪声的污染,采用Jain提出的噪声模型,完成对数化后的小波变换,假设小波系数服从广义高斯分布,估计各尺度的贝叶斯阈值,利用改进的阈值函数处理各小波系数。所用改进阈值函数较软阈值函数有更好的连续性且不易丢失小波系数。处理医学超声图像和声纳超声图像的结果表明,较之以往的去噪方法,该方法在去除噪声的同时能较好的保留边缘及细节特征。

To solve speckle noise in ultrasound imagings, a denoising method was proposed based on an improved BayesShrink threshold. The main reason for ultrasound imaging degeneration is speckle noise. We adopted Jain＇s speckle noise model to carry out our scheme. Wavelet transform coefficients are acquired on coefficients of logarithmically transformed ultrasound imaging. Under the assumption that the statistics of wavelet coefficients is Generalized Gaussian Distribution（GGD）, BayesShrink threshold is calculated for each high frequency subband, and wavelet coefficients are modified using improved threshold method. The improved threshold method is better than soft threshold method in preserving wavelet coefficients owing to its continuity. The results of the experiments show that the method proposed is better than previous ones in preserving edges and details.