Direct comparison of feature tracking and autocorrelation for velocity estimation

Bashford, Gregory R.; Robinson, Desmond

doi:10.1109/tuffc.2007.309

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Significant gray scale features found by t -test with textural differences were extracted and was given to SVM along with the degree of stenosis feature. 116 Feature analysis is performed for a period of time by plaque risk index and can indicate how the feature vector varies as the case changes from asymptomatic to symptomatic. Local binary pattern (LBP) is a grayscale-invariant texture feature calculated from texture of the local neighborhood.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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“…The experimental setup was similar to the one used in our previous studies [21] and is briefly described here. a commercial flow phantom (optimizer rmI 1425, Gammex, middleton, WI) was used to simulate blood flow.…”

Section: Methodsmentioning

confidence: 99%

“…The results suggested that feature tracking exhibits a comparable estimation mean to the conventional commercial method and a favorable estimation variance [21].…”

mentioning

confidence: 91%

Optimal thresholds of feature tracking for blood velocity and tissue motion estimation

Bashford

2009

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Abstract-Feature tracking is an algorithm for estimating blood flow velocity and tissue motion using pulse-echo ultrasound. In contrast to cross-correlation speckle-tracking techniques, feature tracking identifies features at discrete locations and corresponds them from frame to frame. Prior studies have demonstrated that feature-tracking estimates exhibit lower variance than those obtained by the conventional autocorrelation method and require less computational complexity than either speckle tracking or autocorrelation. To date, not much attention has been paid to the process by which trackable features (normally local maxima) are selected from the set of all available features. In the selection process, it is desired to minimize flow estimate variance while providing sufficient spatial and temporal coverage of flow area. Flow studies were performed with a blood flow phantom, 3.5-MHz spherically focused transducer, and a pulser/receiver. Values were selected for the amplitude threshold (based on the RMS value) and width thresholds (based on the wavelength corresponding to transducer center frequency). The performance of this method using different threshold values was evaluated by the estimate standard deviation and number of features available to track. Results show that an optimal width threshold occurs at about 40 to 45% of the transmission wavelength, while a trade-off exists between amplitude thresholds and spatial flow field coverage. Both the standard deviation of estimated velocities and number of available features decrease with increasing threshold (either amplitude or width). This affords a user a method of determining optimal feature tracking thresholds depending on the specific flow application. Judicious selection of feature thresholds can decrease the estimate standard deviation by more than 25%.

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“…Angle-independent CFI, on the other hand, depends on measurement of axial displacements along the axis of two or more ultrasound beams and does not suffer from this problem to the same extent. Langeland et al [38] have compared time domain displacement estimators for 2D tracking, Løvstakken et al [39] have described a more qualitative method which provides an intuitive and detailed display of flow direction, and Bashford and Robinson [40] have compared feature tracking (a variant of speckle tracking) with complex autocorrelation for velocity estimation.…”

Section: Speckle Trackingmentioning

confidence: 99%

Colour flow and motion imaging

Evans

2009

Proc Inst Mech Eng H

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Colour flow imaging (CFI) is an ultrasound imaging technique whereby colour-coded maps of tissue velocity are superimposed on grey-scale pulse-echo images of tissue anatomy. The most widespread use of the method is to image the movement of blood through arteries and veins, but it may also be used to image the motion of solid tissue. The production of velocity information is technically more demanding than the production of the anatomical information, partly because the target of interest is often blood, which backscatters significantly less power than solid tissues, and partly because several transmit-receive cycles are necessary for each velocity estimate. This review first describes the various components of basic CFI systems necessary to generate the velocity information and to combine it with anatomical information. It then describes a number of variations on the basic autocorrelation technique, including cross-correlation-based techniques, power Doppler, Doppler tissue imaging, and three-dimensional (3D) Doppler imaging. Finally, a number of limitations of current techniques and some potential solutions are reviewed.

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Direct comparison of feature tracking and autocorrelation for velocity estimation

Cited by 6 publications

References 20 publications

Carotid artery ultrasound image analysis: A review of the literature

Carotid artery ultrasound image analysis: A review of the literature

Optimal thresholds of feature tracking for blood velocity and tissue motion estimation

Colour flow and motion imaging

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