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超声图像降噪中基于小波变换滤波系统的比较分析。

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images.

机构信息

Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic.

Human Motion Diagnostic Center, Department of Human Movement Studies, University of Ostrava, Ostrava, Czech Republic.

出版信息

PLoS One. 2022 Jul 7;17(7):e0270745. doi: 10.1371/journal.pone.0270745. eCollection 2022.

DOI:10.1371/journal.pone.0270745
PMID:35797331
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9262246/
Abstract

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.

摘要

小波变换（WT）是一种常用于从生物医学图像中抑制噪声和提取特征的方法。WT 系统设置的选择对去噪过程的效率有很大的影响。这项比较研究分析了所提出的 WT 系统在来自多个感兴趣区域的 292 张真实超声图像上的效果。该研究针对两种基本小波基（Daubechies 和 Symlets）的不同尺度函数以及它们对三种噪声污染图像的效率，研究了系统的性能。为了评估我们的广泛分析，我们使用了客观指标，即结构相似性指数（SSIM）、相关系数、均方误差（MSE）、峰值信噪比（PSNR）和通用图像质量指数（Q-index）。此外，本研究还包括临床专家对选定过滤结果的临床见解。结果表明，过滤的效率强烈取决于特定的小波系统设置、超声数据类型和存在的噪声。所提出的研究结果可能为研究人员、软件开发人员和临床专业人员提供有用的指南，以获得高质量的图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11d6/9262246/1ca47502d2a6/pone.0270745.g013.jpg
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