中文摘要：

为有效抑制超声仪器成像中固有的斑点噪声,提出了一种基于非降采样Contourlet变换（nonsubsampled Contourlettransform,NSCT）域中边缘信号系数区提取和最小均方误差（minimum mean square error,MMSE）估计的超声图像的降噪算法。根据NSCT变换的细节信息刻画能力和平移不变性,对其各高频子带中系数进行分类,提取出边缘信号和平缓信号系数区;对超声图像的乘性斑点噪声进行推导研究,在边缘信号系数区和平缓信号系数区,根据各自噪声项的性质分别得出满足贝叶斯最小均方误差估计的降噪滤波方程;最后,对降噪后的系数进行NSCT反变换重建得到降噪图像。仿真图像和临床超声图像的实验结果证实,该算法与传统方法相比,不但能更有效地对斑点噪声进行抑制,也更好地保留了图像的细节信息。

英文摘要：

To effectively suppress the inherent speckle noise in ultrasound imaging equipment,this paper presents a denoising method based on edge signal detecting and MMSE estimation in nonsubsampled Contourlet transform（NSCT） domain.According to the detailed characterization and shift-invariant capabilities of NSCT,the coefficients in high frequency NSCT subbands are classified into edge signal zones and flat signal zones;then the multiplicative speckle noise of ultrasound image in NSCT domain is derived,and the noise reduction filtering equations that meet Bayesian minimum mean square error（MMSE） estimation in edge signal zones and flat signal zones are drawn respectively.Finally the inverse NSCT is applied to the denoised coefficients to reconstruct the denoised image.The experimental results of simulated speckle noise images and real ultrasound images show that the proposed method outperforms several traditional medical ultrasound image denoising methods in terms of speckle noise reduction and detailed information preservation indices.