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. 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. The experimental results show that the proposed method keeps the advantage of the LBF model: effective for inhomogeneous images. Meanwhile the improved model possess other improvements comparing with LBF: robust to the selection of initial contours (size

shape and position) and to noise.