Speckle reducing anisotropic diffusion for 3D ultrasound images

Sun, Qingling; Hossack, John A.; Tang, Jinshan; Acton, Scott T.

doi:10.1016/j.compmedimag.2004.08.001

Cited by 80 publications

(53 citation statements)

References 12 publications

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“…The DoG detector was also implemented according to the original paper by D.G.Lowe [32]. The method is protected under US patent, 3 but it is free for individual research use. The original implementation of the Fast Hessian detector (used in the SURF (Speeded Up Robust Features) detector and descriptor) is closed-source and it is not suitable for research.…”

Section: Methodsmentioning

confidence: 99%

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Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Zukal¹,

Beneš²,

Číka³

et al. 2013

Measurement Science Review

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This paper focuses on the comparison of different interest point detectors and their utilization for measurements in ultrasound (US) images. Certain medical examinations are based on speckle tracking which strongly relies on features that can be reliably tracked frame to frame. Only significant features (interest points) resistant to noise and brightness changes within US images are suitable for accurate long-lasting tracking. We compare three interest point detectors -Harris-Laplace, Difference of Gaussian (DoG) and Fast Hessian -and identify the most suitable one for use in US images on the basis of an objective criterion. Repeatability rate is assumed to be an objective quality measure for comparison. We have measured repeatability in images corrupted by different types of noise (speckle noise, Gaussian noise) and for changes in brightness. The Harris-Laplace detector outperformed its competitors and seems to be a sound option when choosing a suitable interest point detector for US images. However, it has to be noted that Fast Hessian and DoG detectors achieved better results in terms of processing speed.

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Section: Methodsmentioning

confidence: 99%

“…Conversely, there are certain drawbacks some of which are low resolution of the images, speckle noise, scattering and presence of artifacts [2]. Especially the presence of speckle noise [3] in images complicates their further processing.…”

Section: Introduction Mmentioning

confidence: 99%

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Zukal¹,

Beneš²,

Číka³

et al. 2013

Measurement Science Review

View full text Add to dashboard Cite

show abstract

“…In order to allow reasonably accurate block matching, and thus reliable strain measurements, a second stage is commonly performed, following the block matching -smoothing, or regularization of the results [24,25] . The two stage approach employed by most commercial vendors, although inaccurate in principle from a theoretical perspective, is reasonable in cases with sufficiently low speckle decorrelation noise, namely: high frame rates, small regions of search and very good image quality.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of reproducible vendor independent estimation of cardiac function based on first generation speckle tracking echocardiography

Khamis

Shimoni

Hagendorff

et al. 2015

JBEI

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Background: The clinical approval of speckle tracking echocardiography (STE) as an accepted measure of myocardial strain and of LV function is hindered by the discordance of the results among the vendors. Since echocardiography images are noisy, the measured displacements are smoothed or regularized, an operation affecting the strain results. We introduce an "Error-dependent weighted speckle tracking" (EWST) algorithm that allows sensitivity analysis to the different operations affecting noise and accuracy. The aim here was to study whether by modifying the properties of the post block-matching weighted smoothing in the EWST algorithm it was possible to assess the expected inter-vendor strain differences. Methods and results:Forty-eight echocardiographic clips generated by a software-based phantom were used as "gold standard" for validation of the EWST algorithm. Also, a cohort of 435 normal subjects and a cohort of 47 patients, scanned/re-scanned at 2 frame-rates (~70; ~35), were studied using the EWST. The results were compared to those produced by a commercial product of a leading manufacturer (STE LV ). Peak global longitudinal strains [PRLS, (%)] and peak regional longitudinal strains [PRLS, (%)] were calculated and compared. Sensitivity to the region (ROI) determination was tested by shifting the apical endocardial boundary. The differences between the measured PGLS and the ground truth produced by the software-based phantom (average ± standard deviation) were 0.4% ± 0.6% and 1.0% ± 0.7% for the EWST and STE LV , respectively. Normal values were calculated for 435 subjects: -18.82% ± 2.45%, -20.2% ± 5.6%, -19.62% ± 3.62%, 18.77% ± 4.31% by the EWST, and -21.24% ± 2.91%, -26.5% ± 5.0%, -21.1% ± 3.7%, -18.0% ± 3.9%, by the STE LV , respectively, for the PGLS, the peak longitudinal apical, mid-ventricle and basal regions, respectively. A low bias, but significant, was found between PGLS, when calculated for the cohort of 47 patients scanned/re-scanned at 2 frame-rates: -0.80% ± 2.61% and -1.66% ± 2.66% for the EWST and STE LV , respectively. When the apex location (and thus the ROI) was shifted, the bias (mm) (average ± standard deviation) relative to the default position was: 0.82 ± 1.04; 0.61 ± 0.72; -1.06 ± 0.75; and -1.87 ± 0.72, for displacement of 5 cm, 2.5 cm, -2.5 cm, and -5 cm, respectively, for the STE LV . The EWST proved similarly sensitive to the shifting of the apex location. Conclusions: STE is sensitive to the characteristics and amount of smoothing, as well as to the ROI positioning. Modification of the smoothing can produce different stain results, and different distribution of the regional strains. Thus it is preferable to use automatic determination of the ROI and methods that employ minimal smoothing or regularization.

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“…Several well known nonlinear edge preserving filters, such as SNN, Kuwahara (three versions), Cubic, Rational, Sticks, Anisotropic Diffusion, Nonlinear Gaussian are studied for the aim of pre-processing of hyperspectral images [9][10][11][12][13][14][15]. The efficiency of nonlinear filtration is evaluated in terms of the average Improvement in the Peak SNR (PSNR) estimated at the filter output.…”

Section: Introductionmentioning

confidence: 99%