2009
DOI: 10.1016/j.media.2008.10.007
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A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging

Abstract: Displacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours’ displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may b… Show more

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Cited by 72 publications
(71 citation statements)
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“…Individual windows are matched in a 3D search within the reference sweep, using a multiscale exhaustive search for the peak correlation value (as used in [4]). Displacement continuity is enforced within the frame by using a quality guided matching process [5]. The window location estimates are then processed by a Random Sample Consensus (RANSAC) algorithm [9] to exclude outliers, further enforcing continuity.…”
Section: Methodsmentioning
confidence: 99%
“…Individual windows are matched in a 3D search within the reference sweep, using a multiscale exhaustive search for the peak correlation value (as used in [4]). Displacement continuity is enforced within the frame by using a quality guided matching process [5]. The window location estimates are then processed by a Random Sample Consensus (RANSAC) algorithm [9] to exclude outliers, further enforcing continuity.…”
Section: Methodsmentioning
confidence: 99%
“…A large residual indicates that the displacement is not accurate and therefore its influence on the next line should be small, which is realized via the small weight β l . This is, in principle, similar to guiding the displacement estimation based on a data quality indicator [48]. The effect of the tunable parameters α, β a and β l is studied in the Results section.…”
Section: D Analytic Minimization (Am)mentioning
confidence: 97%
“…A large residual indicates that the displacement is not accurate and therefore its influence on the next line should be small, which is realized via the small weight . This is, in principle, similar to guiding the displacement estimation based on a data quality indicator [48]. The effect of the tunable parameters and is studied in Section III.…”
Section: D Analytic Minimization (Am)mentioning
confidence: 99%