Anisotropic diffusion filtering for ultrasound speckle reduction

Guan, Fada; Ton, Phuc; Ge, Shuaiping; Zhao, Lei

doi:10.1007/s11431-014-5483-7

Cited by 36 publications

(15 citation statements)

References 6 publications

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“…The speckle is a particular noise that is found in all ultrasonic images, it is a multiplicative noise, that the echo summations are independent of the coefficient of background reflectivity; it depend only on the micro-relief. In this context, many authors have made contributions to improve speckle filtering techniques on ultrasonic data [7]. The crucial goal in any filtering is to remove the noise without losing the resolution of the image.…”

Section: Methodsmentioning

confidence: 99%

“…The pairing of blocks has a major disadvantage: It requires interpolation for sub-pixel estimation, so the time calculation is longer compared to the other displacement techniques [6]. Recently new techniques based on phase difference have been developed to estimate tissue displacements; however the results are always degraded by speckle noise [7]. …”

Section: Introductionmentioning

confidence: 99%

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Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal

et al. 2017

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BackgroundIn breast ultrasound elastography, tissues displacements estimation is obtained through a technique that follows the evolution of tissues under stress. However, during the acquisition of B-mode images, tissue displacements are often contaminated with multiplicative noise caused by changes in the speckle pattern in the tissue. Thus, the application of monogenic signal technique on the B-mode image in order to estimate displacement tissue, result in a presence of amplified noise in the deformation tissue image, which severely obscures the useful information. In this paper, we propose a new method based on the monogenic features, that is to improve the old monogenic signal (OMS) technique by improving the filtering step, so that the use of an effective denoising technique is enough to ensure a good estimation of displacement tissue. Our proposed method is based on the use of a robust filtering technique combined with the monogenic model.MethodsTwo models of phantom elasticity are used in our test validation sold by CIRS company. In-vivo testing was also performed on the sets of clinical B-mode images to 20 patients including malignant breast tumors. Shrinkage wavelets has been used to eliminate the noise according to the threshold, then a guided filter is introduced to completely filter the image, the monogenic model is used after excerpting the image feature and estimating analytically the displacement tissue.ResultsAccurate and excellent displacement estimation for breast tissue was observed in proposed method results. By adapting our proposed approach to breast B-mode images, we have shown that it demonstrated a higher performance for displacement estimation; it gives better values in term of standard deviation, higher contrast to noise ratio, greater peak signal-to-noise ratio, excellent structural similarity and much faster speed than OMS and B-spline techniques. The results of the proposed model are encouraging, allowing quick and reliable estimations.ConclusionAlthough the proposed approach is used in ultrasound domains, it has never been used in the estimation of the breast tissue displacement. In this context, our proposed approach could be a powerful diagnostic tool to be used in breast displacement estimation in ultrasound elastography.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal

et al. 2017

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“…This gives the distinction between tissue and blood spaces. These resulting images were further filtered with a 2D anisotropic diffusion speckle filter 14,30 to reduce image noise. Based on the quadratic mean images, 30 pixels were randomly chosen from blood spaces, and another 30 pixels were similarly chosen from tissue spaces, and the statistics of the quadratic mean for these pixels was studied, so as to investigate the number of time frames necessary for averaging before sufficient contrast between blood and tissue pixels could be achieved.…”

Section: Image Processingmentioning

confidence: 99%

3D Reconstruction of Chick Embryo Vascular Geometries Using Non-invasive High-Frequency Ultrasound for Computational Fluid Dynamics Studies

et al. 2015

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Recent animal studies have provided evidence that prenatal blood flow fluid mechanics may play a role in the pathogenesis of congenital cardiovascular malformations. To further these researches, it is important to have an imaging technique for small animal embryos with sufficient resolution to support computational fluid dynamics studies, and that is also non-invasive and non-destructive to allow for subject-specific, longitudinal studies. In the current study, we developed such a technique, based on ultrasound biomicroscopy scans on chick embryos. Our technique included a motion cancelation algorithm to negate embryonic body motion, a temporal averaging algorithm to differentiate blood spaces from tissue spaces, and 3D reconstruction of blood volumes in the embryo. The accuracy of the reconstructed models was validated with direct stereoscopic measurements. A computational fluid dynamics simulation was performed to model fluid flow in the generated construct of a Hamburger-Hamilton (HH) stage 27 embryo. Simulation results showed that there were divergent streamlines and a low shear region at the carotid duct, which may be linked to the carotid duct's eventual regression and disappearance by HH stage 34. We show that our technique has sufficient resolution to produce accurate geometries for computational fluid dynamics simulations to quantify embryonic cardiovascular fluid mechanics.

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“…Low spatial frequency indicates uniform composition within the region, and high spatial frequency corresponds to random material composition of the plaque [51]. Since ultrasound images often corrupted with speckle noise [52,53] and wave interference [54], selection of robust features for classification is important. Basically, there exist four types of plaque morphology and its combinations.…”

Section: Vascular Wall Ultrasonic Featuresmentioning

confidence: 99%

A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework

Sharma

Gupta

Kumar

et al. 2015

Curr Atheroscler Rep

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Cardiovascular diseases (including stroke and heart attack) are identified as the leading cause of death in today's world. However, very little is understood about the arterial mechanics of plaque buildup, arterial fibrous cap rupture, and the role of abnormalities of the vasa vasorum. Recently, ultrasonic echogenicity characteristics and morphological characterization of carotid plaque types have been shown to have clinical utility in classification of stroke risks. Furthermore, this characterization supports aggressive and intensive medical therapy as well as procedures, including endarterectomy and stenting. This is the first state-of-the-art review to provide a comprehensive understanding of the field of ultrasonic vascular morphology tissue characterization. This paper presents fundamental and advanced ultrasonic tissue characterization and feature extraction methods for analyzing plaque. Additionally, the paper shows how the risk stratification is achieved using machine learning paradigms. More advanced methods need to be developed which can segment the carotid artery walls into multiple regions such as the bulb region and areas both proximal and distal to the bulb. Furthermore, multimodality imaging is needed for validation of such advanced methods for stroke and cardiovascular risk stratification.

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Anisotropic diffusion filtering for ultrasound speckle reduction

Cited by 36 publications

References 6 publications

Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal

Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal

3D Reconstruction of Chick Embryo Vascular Geometries Using Non-invasive High-Frequency Ultrasound for Computational Fluid Dynamics Studies

A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework

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