目的 LBF（local binary fitting）模型用每个像素点的邻域信息来拟合局部能量，对灰度不均匀图像可以得到很好的分割效果。但是LBF模型只考虑了图像的局部信息，没有考虑全局信息，因此它对初始轮廓大小、形状及位置都非常敏感。针对以上问题，结合全局和局部信息，提出“两阶段”活动轮廓模型。方法 第1阶段，采用退化的CV（Chan-Vese）模型，利用图像的全局信息（灰度均值）快速为图像的目标大致定位；第2阶段，以第1阶段结束时的水平集函数的零水平集为第2阶段的初始轮廓，利用图像的局部信息（局部高斯拟合）得到更加精确的分割结果。结果 实验结果表明，该“两阶段”活动轮廓模型保留了LBF模型分割灰度不均匀图像的能力。结论 改进后的模型较LBF模型对各种初始轮廓（大小、形状、位置）有较强的鲁棒性，以及较强的抗噪性。

Objective The local binary fitting(LBF)model can segment images with intensity inhomogeneity because it fits the local energy by adopting the neighborhood information of each pixel.However, without considering the global information, LBF only considers the local information, which leads to the sensitivity for size, shape and position of the selected initial contours.To solve these problems,a "two-stage" active contour model is proposed by combining local and global information of images in this paper.Method On the first stage, the degenerated Chan-Vese model and the global information (mean gray value) of the image are used to roughly but quickly locate the target.On the second stage, the local information (local Gaussian fitting) is employed to obtain a more accurately segmentation result.The initial contour of stage two is based on the zero level set function at the end of stage one.Result The experimental results show that the proposed method keeps the advantage of the LBF model: effective for inhomogeneous images.Conclusion Meanwhile the improved model possess other improvements comparing with LBF: robust to the selection of initial contours (size, shape and position) and to noise.