Analysis of Carotid Artery Transverse Sections in Long Ultrasound Video Sequences

Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas-Kanade Algorithm

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.

Estimating Arterial Wall Deformations from Automatic Key-Point Detection and Matching

Assessing arterial-wall motion and deformations may reveal pathologic alterations in biomechanical properties of the parietal tissues and, thus, contribute to the detection of vascular disease onset. Ultrasound image sequences allow the observation of this motion and many methods have been developed to estimate temporal changes in artery diameter and wall thickness and to track 2-D displacements of selected points. Some methods enable the assessment of shearing or stretching within the wall, but none of them can estimate all these deformations simultaneously. The method herein proposed was devised to simultaneously estimate translation, compression, stretching and shearing of the arterial wall in ultrasound B-mode image sequences representing the carotid artery longitudinal section. Salient blob-like patterns, called key points, are automatically detected in each frame and matched between successive frames. A robust estimator based on an affine transformation model is then used to assess frame-to-frame motion explaining at best the key-point matches and to reject outliers. Realistic simulated image sequences were used to evaluate the accuracy and robustness of the method against ground truth. The method was also visually assessed on clinical image sequences, for which true deformations are unknown.

Tracking and Monitoring Pulsatility of a Portion of Inferior Vena Cava from Ultrasound Imaging in Long Axis

Pulsatility of the inferior vena cava (IVC) provides information on volume status in healthy subjects and in many clinical conditions. The ultrasound (US) approach to estimating the caval index (CI) is not standardized, as it is operator dependent and vulnerable to measurement errors because of different factors, including movements of the IVC and non-uniform IVC pulsatility along its longitudinal axis. We propose and test in healthy subjects an innovative automated approach, which tracks the IVC movements registered in a B-mode US video clip and estimates the pulsatility of an entire portion of the vein rather than of a single arbitrary section. Large variations in CI estimates were observed along the longitudinal axis (in the worst case, CI ranged between 15% and 60%), indicating the importance of investigating a whole portion of the vessel.