title: [Denoising worm artifacts of elastogram using 2-D wavelet shrinkage].
abstract: 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.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China. cuishaoguo2002@163.com
authors.forename: Shaoguo
authors.initials: S
authors.lastname: Cui
authors.forename: Dongquan
authors.initials: D
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D054459:Elasticity Imaging Techniques / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D058067:Wavelet Analysis