Spatiotemporal filtering of sequences of ultrasound images to estimate a dense field of velocities

Marion, Adrien; Vray, Didier

doi:10.1016/j.patcog.2009.04.020

Cited by 7 publications

(8 citation statements)

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“…where f t is the frame rate, f x the spatial sampling frequency along the lateral direction, and f y the spatial sampling frequency along the axial direction. If necessary, further information is given in [16] on the relation between the orientation in 2D+t volume and velocity.…”

Section: Velocity and Orientation In Space-timementioning

confidence: 99%

“…Equations (1), (2), and (3) show that vector velocity can be estimated from the texture angles θ and ϕ. We know from [16] that the use of a 3D anisotropic Gaussian filter is suitable to estimate orientations from 2D+t ultrasound moving data. In this paper, we propose to develop a bank of 3D quaternionic filters to estimate local orientations and then provide estimated dense motion fields.…”

Section: Spatiotemporal-oriented Filters For Motionmentioning

confidence: 99%

“…These criteria evaluate, for each estimate of v x and v y , the mean errors compared with the theoretical profiles. We defined them in (16):…”

Section: Normalized Meanmentioning

confidence: 99%

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Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging

Marion

Girard

Vray

2010

EURASIP J. Adv. Signal Process.

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Blood motion estimation provides fundamental clinical information to prevent and detect pathologies such as cancer. Ultrasound imaging associated with Doppler methods is often used for blood flow evaluation. However, Doppler methods suffer from shortcomings such as limited spatial resolution and the inability to estimate lateral motion. Numerous methods such as block matching and decorrelation-based techniques have been proposed to overcome these limitations. In this paper, we propose an original method to estimate dense fields of vector velocity from ultrasound image sequences. Our proposal is based on a spatiotemporal approach and considers 2D+t data as a 3D volume. Orientation of the texture within this volume is related to velocity. Thus, we designed a bank of 3D quaternionic filters to estimate local orientation and then calculate local velocities. The method was applied to a large set of experimental and simulated flow sequences with low motion (≈1 mm/s) within small vessels (≈1 mm). Evaluation was conducted with several quantitative criteria such as the normalized mean error or the estimated mean velocity. The results obtained show the good behaviour of our method, characterizing the flows studied.

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Section: Velocity and Orientation In Space-timementioning

confidence: 99%

Section: Spatiotemporal-oriented Filters For Motionmentioning

confidence: 99%

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Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging

Marion

Girard

Vray

2010

EURASIP J. Adv. Signal Process.

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“…Moreover, Jensen and Oddershede [8] proposed a method to jointly estimate the magnitude and the direction of the flow using directional beamforming. Recently, in [9], Marion et al related the velocity to the texture orientation in the spatiotemporal (ST) US volume. This orientation is further estimated using a bank of oriented Gabor filters.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it also contains information about the local orientation of the texture in the images. Note that the monogenic signal is adapted to locally one dimensional images (i1D) and is thus adapted to the spatio-temporal planes used for flow estimation in [9] and herein. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Monogenic orientation-based blood flow estimation in high-frequency ultrasound imaging

Basarab

Vray

Kouamé

2012

2012 IEEE International Ultrasonics Symposium

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Classical blood flow estimation techniques are based on the Doppler effect, but suffer from several limitations: inadequate estimations of low velocities, limited spatial resolution and impossibility to estimate flows perpendicular to the beam axis. In a recent work, the velocity was related to the texture orientation in spatiotemporal planes extracted from an ultrasound 2D+t volume. This texture orientation was previously estimated using a bank of orientated Gabor filters. This represents one of the major drawbacks of this method, as the accuracy of the estimates is directly linked to the angular step between two consecutive filters. We propose a novel velocity estimation method, based on the same assumption that a moving target leaves a trace in the spatio-temporal plane, generating an orientation of the texture related to its velocity. In our approach, we propose to estimate this orientation using the framework of the monogenic signal and thus to avoid the large amount of Gabor filters classically used.

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Motion Estimation and Analysis

Clarysse¹,

Pousin²

2015

Multi‐Modality Cardiac Imaging

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Spatiotemporal filtering of sequences of ultrasound images to estimate a dense field of velocities

Cited by 7 publications

References 20 publications

Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging

Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging

Monogenic orientation-based blood flow estimation in high-frequency ultrasound imaging

Motion Estimation and Analysis

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