Abstract
The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms (P < 0.01). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method (P < 0.05), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method (P > 0.05). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.
Highlights
Used imaging techniques for evaluating cardiac tissue function include Doppler tissue imaging, magnetic resonance imaging (MRI), computerized tomography (CT), and ultrasound
The evaluation of cardiac function by clinical diagnostic technology is mainly the evaluation of left ventricular function, and the research of right ventricle is still relatively vacant in the medical field. e change of right ventricular function is of great significance for the evaluation, diagnosis, and prognosis of pulmonary hypertension and a variety of heart diseases [2]
Lucas–Kanade algorithm has significant advantages in target tracking, it still needs further improvement in cardiac color Doppler ultrasound feature point extraction and motion tracking. erefore, the idea of pyramid was proposed based on the Lucas–Kanade algorithm and the SIFTalgorithm to improve the accuracy and computing speed of the Lucas–Kanade algorithm, which was applied to the right ventricular color Doppler ultrasound feature point extraction and motion tracking
Summary
Received 13 June 2021; Revised 13 July 2021; Accepted 26 July 2021; Published 4 August 2021. E purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. E number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms (P < 0.01). E calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The improved Lucas–Kanade algorithm based on SIFTsignificantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound
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