尽管主动轮廓模型 (Active contour model)，或称 Snakes,近年来已经在计算机视觉和图象处理领域得到了广泛的应用，尤其在边界检测方面也表现出良好的性能，但是由于传统的 Snakes图象边界检测对初始轮廓线的位置十分敏感，因而限制了它的更广泛应用.为了克服这一问题，提出了一种改进型多尺度 DDCM主动轮廓模型的边界检测算法，该算法是首先通过分阶段改变轮廓曲线的内力，使轮廓曲线的曲率能自适应地进行多尺度调整，进而改变了轮廓线的柔性和刚性，使之能够更好地与目标边界匹配.实验结果证明，该算法在计算速度和边界检测精度上，均优于传统的主动轮廓边界检测算法，因而具有一定的实用价值.

In the last decade, Active Contour Model(Snakes) has been successfully applied in the areas of computer vision and image processing. In particular, it reveal much potentials for edge detection and tracking. However, traditional Active Contour Models are very sensitive to the position of initial contour,which limits its further application in edge detection area. To overcome this problem, an accurate and fast multi-scale DDCM active contour model is proposed in this paper, where the curvature of the contour is modulated according to different step during edge detection. We employ this approach to detect the edge of interested objects in CT images. Compared with traditional active contour model, the proposed algorithm has a better performance of edge detection with the aspects of accuracy and speed, which is showed by our experiments.