Speckle characterization methods in ultrasound images – A review

Damerjian, Vera; Tankyevych, Olena; Souag, Nadia; Petit, Éric

doi:10.1016/j.irbm.2014.05.003

Cited by 29 publications

(20 citation statements)

References 138 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Conventionally, ultrasound speckle is considered as undesirable noise that impacts ultrasound image contrast negatively and a main focus of research has been on the removal of ultrasound speckle 31 . Currently, two ultrasound speckle analysis methods have been studied trying to use ultrasound speckle to resolve flow information, including the cross correlation-based ultrasound imaging velocimetry (UIV) 14,15 and the B-mode intensity-based speckle decorrelation method (SDC) 16,17 .…”

Section: Discussionmentioning

confidence: 99%

Functional ultrasound speckle decorrelation-based velocimetry of the brain

Tang

Postnov

Kılıç

et al. 2019

Preprint

View full text Add to dashboard Cite

We present a high-speed, contrast free, quantitative ultrasound velocimetry (vUS) for blood flow velocity imaging of the whole coronal section of rodent brain, complementing the high-speed, non-quantitative functional ultrasound imaging (fUS) and the low-speed, quantitative velocimetry by ultrasound localization microscopy (vULM). We developed the theory for analyzing the normalized first order temporal autocorrelation function of ultrasound speckle dynamics to quantify 'angle-independent' blood flow velocity. Further, by utilizing an ULM spatial constraint on the bulk motion rejected data, vUS provides high resolution blood flow velocity images at a frame rate of 1 frame/s, compared to ~ 2 mins per frame with vULM. vUS was validated with numerical simulation, phantom experiments, and in vivo measurements against vULM. We demonstrated the functional imaging ability of vUS by monitoring blood flow velocity changes during whisker stimulation in awake mice.

show abstract

Section: Discussionmentioning

confidence: 99%

Functional ultrasound speckle decorrelation-based velocimetry of the brain

Tang

Postnov

Kılıç

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The BVF-based feature space used in our rigid registration step could enhance the edge of the tissue and suppress the speckle noise. It is expected that the statistics of the ultrasonography such as the Rayleigh or Nakagami distributions [31] will be incorporated in the algorithms to further improve the robustness of stitching to the noise and intensity variation.…”

Section: Discussionmentioning

confidence: 99%

Image Stitching for Achilles Tendon Ultrasonography Using Particle Swarm Optimization of Boundary Vector Field Based Similarity and Log-Domain Diffeomorphic Registration

Xiong¹,

Zhang²,

Suo³

et al. 2017

J Med Imaging Case Rep

View full text Add to dashboard Cite

“…Shankar [23] considered that the data follows a Nakagami distribution. But more generally, when a large amount of scatterers is locally distributed inside a small area, it has been demonstrated that the statistics of the envelop of the received signal follow a Rayleigh distribution [18,24]. For this reason, some authors proposed to adapt the FMM segmentation framework for the US data analysis by estimating a Rayleigh Mixture Model (RMM) [25,26].…”

Section: Introductionmentioning

confidence: 99%

Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

Tang

Yang

et al. 2017

IET image process

View full text Add to dashboard Cite

As a particular case of the Finite Mixture Model (FMM), Rayleigh Mixture Model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. In this paper, we propose an improved RMM with Neighbour information (RMMN) to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. We evaluated our model on experiments on synthetic data and real US images used by High-Intensity Focused Ultrasound (HIFU) therapy. On this data, we demonstrated that that the proposed model outperforms several state-of-art methods in terms of both segmentation accuracy and computation time.

show abstract

Speckle characterization methods in ultrasound images – A review

Cited by 29 publications

References 138 publications

Functional ultrasound speckle decorrelation-based velocimetry of the brain

Functional ultrasound speckle decorrelation-based velocimetry of the brain

Image Stitching for Achilles Tendon Ultrasonography Using Particle Swarm Optimization of Boundary Vector Field Based Similarity and Log-Domain Diffeomorphic Registration

Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

Contact Info

Product

Resources

About