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Updated: May 30, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

[Denoising worm artifacts of elastogram using 2-D wavelet shrinkage].

Shaoguo Cui¹, Dongquan Liu

¹College of Computer Science, Sichuan University, Chengdu 610065, China. cuishaoguo2002@163.com

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|July 22, 2011

Summary

This summary is machine-generated.

This study introduces a 2-D wavelet shrinkage denoising method to remove worm artifacts in elastograms. The technique effectively enhances elastogram performance, improving signal-to-noise and contrast-to-noise ratios for clearer imaging.

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Last Updated: May 30, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Area of Science:

Medical Imaging
Signal Processing
Biomedical Engineering

Context:

Elastography is a valuable medical imaging technique for assessing tissue stiffness.
Worm artifacts can degrade elastogram quality, hindering accurate diagnosis.
Existing denoising methods may not sufficiently address these artifacts.

Purpose:

To propose and evaluate a 2-D wavelet shrinkage denoising technique for removing worm artifacts in elastograms.
To enhance key elastogram performance indices, including elastographic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe).
To compare the proposed method against 2-D low-pass filtering for artifact reduction and image clarity.

Summary:

A 3-level 2-D discrete wavelet transform with Sym8 wavelet and Birg6-Massart algorithm thresholds was applied to strain estimate matrices.
High-frequency coefficients were quantized using hard and soft threshold functions.
Reconstruction utilized low-frequency coefficients and quantized high-frequency coefficients to denoise the elastogram.

Impact:

The proposed wavelet shrinkage method effectively denoises worm artifacts, significantly improving SNRe and CNRe.
It enhances the correlation between denoised and ideal elastograms.
The technique offers superior performance over 2-D low-pass filtering, providing clearer lesion edges and suppressing artifacts across various strains.