Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

Tenbrinck, Daniel; Schmid, Sönke; Jiang, Xiaoyi; Schäfers, Klaus P.; Stypmann, Jörg

doi:10.1007/s10851-012-0398-z

Cited by 20 publications

(11 citation statements)

References 21 publications

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“…Recent studies have applied HOG to characterize textures in US images . Up to the present, however, the main purpose of applying this method on US images has been macrostructure detection such as nuchal translucency or motion estimation . We decided to include the HOG method in our study, since it may provide useful information related to tissue histologic characteristics.…”

Section: Methodsmentioning

confidence: 99%

“…36,37 Up to the present, however, the main purpose of applying this method on US images has been macrostructure detection such as nuchal translucency 38 or motion estimation. 39 We decided to include the HOG method in our study, since it may provide useful information related to tissue histologic characteristics. The HOG method counts frequencies of gradient orientation values in localized portions of an image.…”

Section: Histogram Of Oriented Gradientsmentioning

confidence: 99%

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Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Perez‐Moreno

Domínguez

Gratacós

et al. 2018

J of Ultrasound Medicine

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Objectives To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. Methods We compared, ranked, and validated the robustness of 5 texture‐based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non‐US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot‐Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non‐US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. Results Three of the 5 methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) had favorably robust performance when using the non‐US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). Conclusions Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.

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Section: Methodsmentioning

confidence: 99%

Section: Histogram Of Oriented Gradientsmentioning

confidence: 99%

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Perez‐Moreno

Domínguez

Gratacós

et al. 2018

J of Ultrasound Medicine

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“…where ε is a Gaussian noise as in (1) and γ > 0. Model (2) seems to be more appropriate as it preserves the signal dependency and has shown been to be effective for speckle modelling [2], as well as motion estimation in US image sequences [21]. In this paper, we develop an appropriate wavelet thresholding method assuming model (2) holds true.…”

Section: Hybrid Noisementioning

confidence: 99%

Hyperbolic Wavelet-Fisz Denoising for a Model Arising in Ultrasound Imaging

Farouj

Freyermuth

Navarro

et al. 2017

IEEE Trans. Comput. Imaging

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We present an algorithm and its fully data-driven extension for noise reduction in ultrasound imaging. Our proposed method computes the hyperbolic wavelet transform of the image, before applying a multiscale variance stabilization technique, via a Fisz transformation. This adapts the wavelet coefficients statistics to the wavelet thresholding paradigm. The aim of the hyperbolic setting is to recover the image while respecting the anisotropic nature of structural details. The data-driven extension removes the need for any prior knowledge of the noise model parameters by estimating the noise variance using an isotonic Nadaraya-Watson estimator. Experiments on synthetic and real data, and comparisons with other noise reduction methods demonstrate the potential of our method at recovering ultrasound images while preserving tissue details. Finally, we emphasize the noise model we consider by applying our variance estimation procedure on real images.

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“…Several models have received extensive attention in the ultrasound engineering community, such as block matching (BM) [20][21][22][23], optical flow [24][25][26], elastic registration [27,28], and machine learning models [29,30]. The most computationally efficient method of quantifying tissue motion on ultrasound is BM.…”

Section: Introductionmentioning

confidence: 99%

An optimisation-based iterative approach for speckle tracking echocardiography

Azarmehr

Howes

et al. 2020

Med Biol Eng Comput

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Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking.

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Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

Cited by 20 publications

References 21 publications

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Hyperbolic Wavelet-Fisz Denoising for a Model Arising in Ultrasound Imaging

An optimisation-based iterative approach for speckle tracking echocardiography

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