融合C-V和GVF的测地线活动轮廓模型

对于有凹陷边界或弱边界的待分割目标，采用传统的测地线活动轮廓(GAC)模型无法进行准确的图像分割.为了解决这一问题，提出了一种融合C-V模型、GVF模型和GAC模型的图像分割算法.在该算法中，GAC模型的单位内法向量与GVF模型的梯度矢量流共同作用，促使轮廓曲线向目标的边界方向运动；而GAC模型单位内法向量与C-V模型的区域信息的力场共同作用，不仅促使轮廓曲线向目标的边界方向运动，而且使轮廓曲线稳定在目标的边界上.仿真实验证明了上述方法的有效性，同时还证明了该方法对轮廓曲线的初始位置具有较好的适应性.

Geodesic Active Contour Model Combined with C-V and GVF

An image with concave edges or weak edges cannot be segmented precisely using the conventional geodesic active contour (GAC) method. So, a novel image segmentation algorithm was proposed by combining the C-V and GVF models with the GAC model. In this algorithm, the unit inward normal of the GAC model was joined to the gradient vector flow of the GVF model, moving the contour curve towards the boundary of the object. Also, it was joined to the region information of the C-V model, getting the curve not only to move to but also to stay at the boundary of the object. Simulation results show that the algorithm proposed is effective and robust to the initial location of the contour curve.