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首页> 外文期刊>Quality Control, Transactions >Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview
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Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

机译：超声图像中的斑块降噪，以改善生物医学应用的计量评估：概述

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摘要

In recent years, many studies have examined filters for eliminating or reducing speckle noise, which is inherent to ultrasound images, in order to improve the metrological evaluation of their biomedical applications. In the case of medical ultrasound images, said noise can produce uncertainty in the diagnosis because details, such as limits and edges, should be preserved. Most algorithms can eliminate speckle noise, but they do not consider the conservation of these details. This paper describes, in detail, 27 techniques that mainly focus on the smoothing or elimination of speckle noise in medical ultrasound images. The aim of this study is to highlight the importance of improving said smoothing and elimination, which are directly related to several processes (such as the detection of regions of interest) described in other articles examined in this study. Furthermore, the description of this collection of techniques facilitates the implementation of evaluations and research with a more specific scope. This study initially covers several classical methods, such as spatial filtering, diffusion filtering, and wavelet filtering. Subsequently, it describes recent techniques in the field of machine learning focused on deep learning, which are not yet well known but greatly relevant, along with some modern and hybrid models in the field of speckle-noise filtering. Finally, five Full-Reference (FR) distortion metrics, common in filter evaluation processes, are detailed along with a compensation methodology between FR and Non-Reference (NR) metrics, which can generate greater certainty in the classification of the filters by considering the information of their behavior in terms of perceptual quality provided by NR metrics.
机译：近年来，许多研究已经检查了用于消除或减少散斑噪声的过滤器，这是超声图像固有的，以改善其生物医学应用的计量评估。在医学超声图像的情况下，所述噪声可以在诊断中产生不确定性，因为应保留细节，例如限制和边缘。大多数算法可以消除斑点噪音，但它们不考虑保护这些细节。本文详细描述了27种技术，主要关注医疗超声图像中的斑点噪声的平滑或消除。本研究的目的是突出改善所述平滑和消除的重要性，这与本研究中检测的其他文章中描述的若干过程直接相关（例如检测到的感兴趣区域）。此外，对该技术集合的描述有助于利用更具体的范围实现评估和研究。本研究最初涵盖了几种经典方法，例如空间滤波，扩散滤波和小波滤波。随后，它描述了专注于深度学习的机器学习领域的最新技术，这尚不熟悉，而且具有很大的相关性，以及斑点噪声滤波领域的一些现代和混合模型。最后，在过滤器评估过程中常见的五个完整参考（FR）失真度量，并在FR和非参考（NR）度量之间进行了补偿方法，这可以通过考虑到滤波器的分类来产生更大的确定性在NR指标提供的感知质量方面的行为信息。

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