Shanghai Institute for Advanced Communication and Data Science, The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai University, Shanghai, China.
Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Phys Eng Sci Med. 2021 Mar;44(1):207-219. doi: 10.1007/s13246-020-00969-x. Epub 2021 Jan 26.
Speckle noise contaminates medical ultrasound images, and the suppression of speckle noise is helpful for image interpretation. Traditional ultrasound denoising (i.e., despeckling) methods are developed on two-dimensional static images. However, one of the advantages of ultrasonography is its nature of dynamic imaging. A method for dynamic ultrasound despeckling is expected to incorporate both the spatial and temporal information in successive images of dynamic ultrasound and thus yield better denoising performance. Here we regard a dynamic ultrasound video as three-dimensional (3-D) images with two dimensions in the spatial domain and one in the temporal domain, and we propose a despeckling algorithm for dynamic ultrasound named the 3-D Gabor-based anisotropic diffusion (GAD-3D). The GAD-3D expands the classic two-dimensional Gabor-based anisotropic diffusion (GAD) into 3-D domain. First, we proposed a robust 3-D Gabor-based edge detector by capturing the edge with 3-D Gabor transformation. Then we embed this novel detector into the partial differential equation of GAD to guide the 3-D diffusion process. In the simulation experiment, when the noise variance is as high as 0.14, the GAD-3D improves the Pratt's figure of merit, mean structural similarity index and peak signal-to-noise ratio by 24.32%, 10.98%, and 6.51%, respectively, compared with the best values of seven other methods. Experimental results on clinical dynamic ultrasonography suggest that the GAD-3D outperforms the other seven methods in noise reduction and detail preservation. The GAD-3D is effective for dynamic ultrasound despeckling and may be potentially valuable for disease assessment in dynamic medical ultrasonography.
斑点噪声污染了医学超声图像，抑制斑点噪声有助于图像解释。传统的超声去噪（即去斑）方法是基于二维静态图像开发的。然而，超声成像的一个优点是其动态成像的特性。期望一种动态超声去噪方法能够结合动态超声连续图像中的空间和时间信息，从而获得更好的去噪性能。在这里，我们将动态超声视频视为具有两个空间维度和一个时间维度的三维（3-D）图像，我们提出了一种名为三维基于 Gabor 的各向异性扩散（GAD-3D）的动态超声去噪算法。GAD-3D 将经典的二维基于 Gabor 的各向异性扩散（GAD）扩展到 3-D 域。首先，我们通过使用 3-D Gabor 变换捕获边缘，提出了一种鲁棒的 3-D Gabor 边缘检测器。然后，我们将这个新的检测器嵌入到 GAD 的偏微分方程中，以指导 3-D 扩散过程。在模拟实验中，当噪声方差高达 0.14 时，与其他七种方法的最佳值相比，GAD-3D 分别将 Pratt 的度量、平均结构相似性指数和峰值信噪比提高了 24.32%、10.98%和 6.51%。对临床动态超声的实验结果表明，GAD-3D 在降噪和细节保留方面优于其他七种方法。GAD-3D 对动态超声去噪有效，可能对动态医学超声中的疾病评估具有潜在价值。
