Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging

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...

Full description

Bibliographic Details
Main Authors: Adrien Marion, Patrick Girard, Didier Vray
Format: Article
Language:English
Published: SpringerOpen 2010-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/2010/693218
id doaj-a0abc7bb1abb4a8792123f1b88a65cab
record_format Article
spelling doaj-a0abc7bb1abb4a8792123f1b88a65cab2020-11-24T22:17:22ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-01201010.1155/2010/693218Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound ImagingAdrien MarionPatrick GirardDidier VrayBlood 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. http://dx.doi.org/10.1155/2010/693218
collection DOAJ
language English
format Article
sources DOAJ
author Adrien Marion
Patrick Girard
Didier Vray
spellingShingle Adrien Marion
Patrick Girard
Didier Vray
Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging
EURASIP Journal on Advances in Signal Processing
author_facet Adrien Marion
Patrick Girard
Didier Vray
author_sort Adrien Marion
title Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging
title_short Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging
title_full Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging
title_fullStr Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging
title_full_unstemmed Quaternionic Spatiotemporal Filtering for Dense Motion Field Estimation in Ultrasound Imaging
title_sort quaternionic spatiotemporal filtering for dense motion field estimation in ultrasound imaging
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2010-01-01
description 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.
url http://dx.doi.org/10.1155/2010/693218
work_keys_str_mv AT adrienmarion quaternionicspatiotemporalfilteringfordensemotionfieldestimationinultrasoundimaging
AT patrickgirard quaternionicspatiotemporalfilteringfordensemotionfieldestimationinultrasoundimaging
AT didiervray quaternionicspatiotemporalfilteringfordensemotionfieldestimationinultrasoundimaging
_version_ 1725785058361475072