Department of Biomedical Engineering, School of Life Science and Technology, Ministry of Education Key Laboratory of Molecular Biophysics, Huazhong University of Science and Technology, Wuhan, P.R. China.
School of Naval Architecture & Ocean Engineering, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, P. R. China.
PLoS One. 2018 Oct 12;13(10):e0205390. doi: 10.1371/journal.pone.0205390. eCollection 2018.
Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.
斑点减少仍然是超声图像处理和分析的一个关键问题。由于其具有竞争力的去斑性能，最近非局部均值（NLM）滤波器引起了广泛关注。然而，现有的 NLM 方法通常通过直接利用噪声图像的灰度信息来确定两个补丁之间的相似性，这使得很难有效地表示超声图像的结构相似性。为了解决这个问题，通过引入从该网络提取的图像补丁的固有特征而不是像素强度到像素相似性计算中，提出了基于简单深度学习基线 PCANet 的 NLM 方法。在这种方法中，通过使用参数修正线性单元（PReLU）激活函数代替原始 PCANet 中的二进制哈希和块直方图，提出了改进的两阶段 PCANet。该模型首先在超声数据库上进行训练，以学习卷积核。然后，利用训练好的 PCANet 从要去斑的噪声图像的预去噪版本中提取图像补丁的固有特征。这些获得的特征被连接在一起，以确定 NLM 方法中图像补丁之间的结构相似性，根据该相似性，在搜索窗口中计算所有像素的加权平均值，以生成最终的去斑图像。已经在各种图像上进行了广泛的实验，以证明该方法优于几种著名的去斑算法和基于 ReLU 函数和 sigmoid 函数的 PCANet 基于 NLM 方法。视觉检查表明，该方法在减少斑点噪声和保留图像细节方面优于比较方法。定量比较表明，在所评估的所有方法中，我们的方法对合成图像产生了最佳的结构相似性指数度量（SSIM）值，对模拟图像和临床超声图像产生了最高的等效视数（ENL）值。
