title: An automated segmentation method for three-dimensional carotid ultrasound images.
abstract: 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.
authors.affiliation: The John P Robarts Research Institute, London, Ontario, Canada.
authors.forename: A
authors.initials: A
authors.lastname: Zahalka
authors.forename: A
authors.initials: A
authors.lastname: Fenster
mesh_terms: D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D012984:Software; D014463:Ultrasonography / Q000379:methods*