摘要 : A method is proposed for generation of the centerline of 3D tubular shapes using an extensible-skeleton model. Starting from a user-selected point, the skeleton grown by iteratively adding subsequent centerline points within a pre... 展开 A method is proposed for generation of the centerline of 3D tubular shapes using an extensible-skeleton model. Starting from a user-selected point, the skeleton grown by iteratively adding subsequent centerline points within a prediction-estimation scheme controlled by a multi-scale analysis of the image moments. The location of the next point is predicted according the local orientation of the tubular structure. The coordinates of the predicted point are corrected under the influence of image forces and of prior model shape constraints. The extraction of artery centerlines from magnetic resonance angiography (MRA) images is described. The goal is a quantitative assessment of arterial stenoses based on cross-sectional diameters and areas of the vessel contours in the planes locally perpendicular to the centerline. For this purpose, iso-contours extraction based on an adaptive local iso-value have been implemented. The robustness and accuracy of the method have been demonstrated on MRA data on 5 reference phantoms and on 17 patients' carotid arteries, 97% of the centerlines were exploitable in the carotid arteries (100% in the phantoms). On average, the centerlines were extracted within 1 second, and the whole quantification process took less than 1 minute per artery, including interaction and display. The Mean difference (± standard deviation) between stenosis percentages, semi-automatically measured and visually estimated by radiologists, was 0.23% ± 7.89%. The reproducibility of the semi-automatic method was significantly better. 收起