Abstract
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.
Highlights
The metrological evaluation of biomedical equipment, in terms of patient safety, is important in order to avoid adverse events
This paper presented an overview of speckle noise filtering methods implemented in US images for improving the accuracy of their results, which has an impact on legal metrology for healthcare services focused on patient safety
It is noteworthy that Speckle filtering has different trends according to the groups in which it is analyzed, for example, in the diffusion group they have focused on looking for the function of diffusion coefficients that best allow the identification of edges, in order to separate regions that can be filtered from those that cannot
Summary
The metrological evaluation of biomedical equipment, in terms of patient safety, is important in order to avoid adverse events. This paper presents an overview of the concept of speckle noise in US images and the most common and important filtering techniques, along with a general discussion of their biomedical applications It includes some recent techniques in the field of machine intelligence that, not yet well known, have become more relevant, as well as some modern and hybrid modalities in the field of speckle noise filtering in US images. It presents a formal description of these methods, in order to enable the development of applications with a suitable metrological approach, detailing some common metrics in the filter evaluation process. A compensation methodology between FR and NR measurements is discussed, which enables a more detailed comparison of the implemented filters
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