基于局部方差改进的超声图像各向异性扩散去噪算法

针对各向异性扩散算法不能有效区分强噪声和弱边缘的缺点,提出了一种基于图像局部统计特征改进的算法。该算法在对图像进行各向异性扩散去噪的过程中,使用梯度阈值找到图像中灰度变化较大的点,再通过计算局部方差和局部去心方差的差值判断该点是否为噪声点,若是噪声点则使用均值滤波处理。对仿真图像和临床超声图像的实验结果表明:与传统的各向异性扩散算法相比,改进的算法在图像去噪和特征保留的能力上得到了良好的提升。

Local variance based anisotropic diffusion denoising method for ultrasonic image

Since the anisotropic diffusion methods cannot make a distinction between strong noise and weak edge effectively, the authors proposed an improved anisotropic diffusion denoising method based on local statistical characteristics. While denoising images by anisotropic diffusion method, points with large gray-level variations were found by using gradient threshold, and whether the point was a noise point or not was judged by calculating local variance and local deleted variance, and then mean filtering was used for the noise points. The experiments upon simulation images and clinical ultrasonic images show that this method preserves features and edges more efficiently than traditional anisotropic diffusion methods while denoising images.