Segmentation of carotid artery in ultrasound images: method development and evaluation technique.
Segmentation of carotid artery lumen in two-dimensional and three-dimensional ultrasonography is an important step in computerized evaluation of arterial disease severity and in finding vulnerable atherosclerotic plaques susceptible to rupture causing stroke. Because of the complexity of anatomical structures, noise as well as the requirement of accurate segmentation, interactions are necessary between observers and the computer segmentation process. In this paper a segmentation process is described based on the deformable model method with only one seed point to guide the initialization of the deformable model for each lumen cross section. With one seed, the initial contour of the deformable model is generated using the entropy map of the original image and mathematical morphology operations. The deformable model is driven to fit the lumen contour by an internal force and an external force that are calculated, respectively, with geometrical properties of deformed contour and with the image gray level features. The evaluation methodology using distance-based and area-based metrics is introduced in this paper. A contour probability distribution (CPD) method for calculating distance-based metrics is introduced. The CPD is obtained by generating contours of the lumen using a set of possible seed locations. The mean contour can be compared to a manual outlined contour to provide accuracy metrics. The variance computed from the CPD can provide metrics of local and global variability. These metrics provide a complete performance evaluation of an interactive segmentation algorithm and a means for comparing different algorithm settings.
Mao
F
F
Imaging Research Laboratories, John P. Robarts Research Institute, University of Western Ontario, London, Canada.
Gill
J
J
Downey
D
D
Fenster
A
A
D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D014463:Ultrasonography / Q000379:methods*