
pmid: 18467224
Feature tracking was developed to efficiently compute motion measurements from volumetric ultrasound images. Prior studies have demonstrated the motion magnitude accuracy and computation speed of feature tracking. However, the previous feature tracking implementations were limited by performance of their calculations in rectilinear coordinates. Also, the previous feature tracking approaches did not fully explore the three dimensional (3- D) nature of volumetric image analysis or utilize the 3-D directional information from the tracking calculations. This study presents an improved feature tracking method which achieves further computation speed gains by performing all calculations in the native spherical coordinates of the 3-D ultrasound image. The novel method utilizes a statistical analysis of tracked directions of motion to achieve better rejection of false tracking matches. Results from in vitro tracking of a speckle target show that the new feature tracking method is significantly faster than correlation search and can accurately determine target motion magnitude and 3-D direction.
Movement, Reproducibility of Results, Image Enhancement, Sensitivity and Specificity, Pattern Recognition, Automated, Imaging, Three-Dimensional, Artificial Intelligence, Image Interpretation, Computer-Assisted, Algorithms, Ultrasonography
Movement, Reproducibility of Results, Image Enhancement, Sensitivity and Specificity, Pattern Recognition, Automated, Imaging, Three-Dimensional, Artificial Intelligence, Image Interpretation, Computer-Assisted, Algorithms, Ultrasonography
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 16 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
