Cui Shaoguo, Liu Dongquan
College of Computer Science, Sichuan University, Chengdu 610065, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Jun;28(3):460-4.
This paper proposes a technique to denoise the worm artifacts of elastogram using 2-D wavelet shrinkage denoising method. Firstly, strain estimate matrix including worm artifacts was decomposed to 3 levels by 2-D discrete wavelet transform with Sym8 wavelet function, and the thresholds were obtained using Birg6-Massart algorithm. Secondly, all the high frequency coefficients on different levels were quantized by using hard threshold and soft threshold function. Finally, the strain estimate matrix was reconstructed by using the 3rd layer low frequency coefficients and other layer quantized high frequency coefficients. The simulation results illustrated that the present technique could efficiently denoise the worm artifacts, enhance the elastogram performance indices, such as elastographic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe), and could increase the correlation coefficient between the denoised elastogram and the ideal elastogram. In comparison with 2-D low-pass filtering, it could also obtain the higher elastographic SNRe and CNRe, and have clearer hard lesion edge. In addition, the results demonstrated that the proposed technique could suppress worm artifacts of elastograms for various applied strains. This work showed that the 2-D wavelet shrinkage denoising could efficiently denoise the worm artifacts of elastogram and enhance the performance of elastogram.
本文提出一种使用二维小波收缩去噪方法去除弹性图蠕虫伪影的技术。首先，利用具有Sym8小波函数的二维离散小波变换将包含蠕虫伪影的应变估计矩阵分解为3层，并使用Birg6 - Massart算法获得阈值。其次，使用硬阈值和软阈值函数对不同层的所有高频系数进行量化。最后，利用第三层低频系数和其他层量化后的高频系数重建应变估计矩阵。仿真结果表明，该技术能够有效去除蠕虫伪影，提高弹性图性能指标，如弹性图信噪比（SNRe）和弹性图对比噪声比（CNRe），并能提高去噪后的弹性图与理想弹性图之间的相关系数。与二维低通滤波相比，它还能获得更高的弹性图SNRe和CNRe，且硬病变边缘更清晰。此外，结果表明所提出的技术可以抑制各种应用应变下弹性图的蠕虫伪影。这项工作表明，二维小波收缩去噪能够有效去除弹性图的蠕虫伪影并提高弹性图的性能。
