Key result
Novel SVM method improves TDE accuracy over state-of-the-art techniques by classifying peak-hopping and jitter errors.
Why the study?
The accuracy of time-delay estimation in ultrasound elastography is limited by peak-hopping and jitter errors affecting normalized cross correlation values.
A novel machine learning-based method improves the accuracy of time-delay estimation in ultrasound elastography by better classifying peak-hopping and jitter errors.
Answers are grounded in this paper and cite their sources.
Proposed TDE accuracy metric addresses NCC limitations in elastography; leaves open prospective validation before clinical adoption.
The accuracy of time-delay estimation (TDE) in ultrasound elastography is usually measured by calculating the value of normalized cross correlation (NCC) at the estimated displacement. NCC value, however, could be very high at a displacement estimate with large error, a well-known problem in TDE referred to as peak-hopping. Furthermore, NCC value could suffer from jitter error, which is due to electric noise and signal decorrelation. Herein, we propose a novel method to assess the accuracy of TDE by investigating the NCC profile around the estimated time-delay. We extract several features from the NCC profile, and utilize support vector machine to classify peak-hopping and jitter error. The results on simulation, phantom, and in vivo data show the significant improvement of the proposed algorithm compared to the state of the art techniques.
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Amidabadi et al. (2017) studied this question. Supervised classification using support vector machine vs. State of the art techniques was evaluated on Accuracy of time-delay estimation (TDE). A novel method utilizing support vector machine to classify peak-hopping and jitter error significantly improved the accuracy of time-delay estimation compared to state-of-the-art techniques.
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