Speckle Suppression in Ultrasonic Images Based on Undecimated Wavelets

Argenti, Fabrizio; Torricelli, Gionatan

doi:10.1155/s1110865703211136

Cited by 22 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [20], based on the experimental estimation of the mean versus the standard deviation in Log-compressed images, Loupas et al have shown that c = 0.5 model fits better to data than the multiplicative model or the Rayleigh model. Since, this model has been used successfully in many studies [2,11,16,29]. Clearly, this model is relevant since it is confirmed that the speckle is higher in regions of high intensities versus regions of low intensities [16,26].…”

Section: Bayesian Nl-meansmentioning

confidence: 92%

Real time ultrasound image denoising

Fontes

Barroso

Coupé

et al. 2010

J Real-Time Image Proc

View full text Add to dashboard Cite

International audienceImage denoising is the process of removing the noise that perturbs image analysis methods. In some applications like segmentation or registration, denoising is intended to smooth homogeneous areas while preserving the contours. In many applications like video analysis, visual servoing or image-guided surgical interventions, real-time denoising is required. This paper presents a method for real-time denoising of ultrasound images: a modified version of the NL-means method is presented that incorporates an ultrasound dedicated noise model, as well as a GPU implementation of the algorithm. Results demonstrate that the proposed method is very efficient in terms of denoising quality and is real-time

show abstract

Section: Bayesian Nl-meansmentioning

confidence: 92%

Real time ultrasound image denoising

Fontes

Barroso

Coupé

et al. 2010

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…Recent studies into US images also demonstrate that the distribution of noise is satisfactorily approximated through a gamma distribution [65] or a Fisher-Tippett distribution [66]. Therefore, we adopted Loupas noise model [67], which has been successfully used in many studies [68]- [70], because it is more flexible and less restrictive than the usual Radio Frequency model; furthermore, it can capture reliable image statistics since factor γ depends on the US devices and the additional processing related to image formation. Furthermore, Loupas et al demonstrated that, with γ = 0.5, the model fits the data better than multiplicative or Rayleigh models.…”

Section: Nl(u)(mentioning

confidence: 99%

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

et al. 2020

View full text Add to dashboard Cite

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. INDEX TERMS Diffusion filtering, image pre-processing, metrological evaluation, spatial filtering, speckle noise, ultrasound images, wavelet filtering.

show abstract

“…Whenever f · u w, as it happens for SAR speckle, it stems that γ eq (f ) → 1 − . In practice, the left-hand side of (2), i.e., (1) with γ = 1, is taken as a noise model suitable for ultrasonic images [10].…”

Section: Signal-dependent Noise Modelingmentioning

confidence: 99%

Unsupervised estimation of signal-dependent CCD camera noise

Aiazzi

Alparone

Baronti

et al. 2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article deals with an original method to estimate the noise introduced by optical imaging systems, such as CCD cameras. The power of the signal-dependent photon noise is decoupled from the power of the signal-independent electronic noise. The method relies on the multivariate regression of sample mean and variance. Statistically similar image pixels, not necessarily connected, produce scatterpoints that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a commercial CCD camera highlight the accuracy of the proposed method and its applicability to separate R-G-B components that have been corrected for the nonlinear effects of the camera response function, but not yet interpolated to the the full size of the mosaiced R-G-B image.

show abstract

Speckle Suppression in Ultrasonic Images Based on Undecimated Wavelets

Cited by 22 publications

References 28 publications

Real time ultrasound image denoising

Real time ultrasound image denoising

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

Unsupervised estimation of signal-dependent CCD camera noise

Contact Info

Product

Resources

About