Abstract: Edge information is crucial for image segmentation. A novel geometric model is proposed, which incorporates edge information into C-V model（active contours without edges）. It utilizes both the information of homogeneous regions and the edge information to stop the active contours on the object boundaries. The experimental results show that the proposed model can overcome some disadvantages of C-V model, and obtain better results with respect to images that have the intensity inhomogeneity in objects or backgrounds, weak edges, and/or high noises while significantly reducing segmentation time. Besides, it has many advantages over other two improved C-V models（LBF and GACV）.

Key words: image segmentation, geometric active contour model, C-V model, level set method, partial differential equation

摘要： 边缘信息对图像分割是十分重要的。把图像的边缘信息融入C-V模型（active contours without edges），提出一个新的几何模型，它同时利用同质区域信息和边缘信息使演化曲线在目标边缘处停止。实验显示：新模型能够克服C-V模型的一些缺点；在减少分割时间的同时，对目标灰度不均匀或背景灰度不均匀、含弱边缘或强噪声的图像，分割效果不仅优于C-V模型，也优于C-V模型的两个最新改进模型（LBF和GACV）。

关键词: 图像分割, 几何活动轮廓模型, C-V模型, 水平集方法, 偏微分方程