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.

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical applications: an Overview

Noise in digital images is the major cause of severe artifacts. Filter design for denoising applications can also be addressed with optimization techniques as conventional filters incur in this. Exploration and Exploitation capability features of the Meta Heuristic Optimization Techniques make them applicable to noise reduction in digital images. An increasing number of Meta Heuristic Optimization algorithms make it suitable for designing FIR filters. In the proposed method, Particle Swarm Optimization, a global optimizer algorithm was used in calculating the appropriate coefficients for 2D FIR Filter. The proposed filter was applied to standard test images for testing its noise suppression capability. Indicators of performance, such as Peak signal to noise ratio (PSNR) values and Structural Content (SC) were used in accessing the efficiency of the proposed method and to the adaptability of the method for removing different noise types. Thus a brief comparison for noise suppression in digital images with both multiplicative and additive noise types using PSO optimized 2D FIR filter is addressed in this paper.

Analysis of Particle Swarm Optimization Based 2D FIR Filter for Reduction of Additive and Multiplicative Noise in Images

A Novel approach in Hybrid Median Filtering for Denoising Medical images

Ultrasound is a well-known medical imaging modality on its ability to visualize kidney, liver and pancreas accurately. However, speckle noise, caused by constructive and destructive interferences of the wavelets will disturb the image of the organs. The main goal of this paper is to identify the most suitable and reasonable filtering technique for each organ. 50 ultrasound images for each organ- kidney, liver and pancreas are taken using Toshiba Nemio 10 ultrasound machine with 3.75 MHz PVM-375AT transducer. The images are cropped into specific sizes before converted into grayscale image. Each of the images then filtered using median filter, Wiener filter, 2-D order-statistic filter, N-D filter, and Entropy filter. Performance of each filter are analysed with MSE and PSNR. The results show that Median filter is the most suitable filter for kidney and pancreas while Wiener filter is the most effective filter in removing noise for liver.

Filtering technique in ultrasound for kidney, liver and pancreas image using Matlab

RED-MAM: A residual encoder-decoder network based on multi-attention fusion for ultrasound image denoising

Over the last three decades, several despeckling filters have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information This paper compiles and compares well-known techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images A comparison of the methods studied is done based on an experiment, using quality metrics, texture analysis and interpretation of profiles to evaluate their performance and show the benefits each one can contribute to denoising and feature preservation To test the methods, a noise-free image of a kidney is used and then the Field II program simulates a B-mode ultrasound image By this way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference In this study, a total of 17 different speckle reduction algorithms have been documented based on spatial filtering, diffusion filtering and wavelet filtering, with 15 qu

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R. Rosa

Papers

Performance analysis of speckle ultrasound image filtering