An automated segmentation method for 3D vascular ultrasound images performed segmentation within 3% of the true cross-sectional area on stenosed vessel phantoms.
The developed automated segmentation method for 3D vascular ultrasound is fast and highly accurate in phantom models, with demonstrated feasibility in patient carotid images.
We have developed an automated segmentation method for three-dimensional vascular ultrasound images. The method consists of two steps: an automated initial contour identification, followed by application of a geometrically deformable model (GDM). The formation of the initial contours requires the input of a single seed point by the user, and was shown to be insensitive to the placement of the seed within a structure. The GDM minimizes contour energy, providing a smoothed final result. It requires only three simple parameters, all with easily selectable values. The algorithm is fast, performing segmentation on a 336 x 352 x 200 volume in 25 s when running on a 100 MHz 9500 Power Macintosh prototype. The segmentation algorithm was tested on stenosed vessel phantoms with known geometry, and the segmentation of the cross-sectional areas was found to be within 3% of the true area. The algorithm was also applied to two sets of patient carotid images, one acquired with a mechanical scanner and the other with a freehand scanning system, with good results on both.
Zahalka et al. (Wed,) conducted a other in Carotid ultrasound imaging. Automated segmentation method using geometrically deformable model (GDM) vs. Known geometry (true area) was evaluated on Cross-sectional area segmentation accuracy. An automated segmentation method for 3D vascular ultrasound images performed segmentation within 3% of the true cross-sectional area on stenosed vessel phantoms.