by traditional level set based segmentation models. In this paper, we propose a new level set

method integrating local and global intensity information adaptively to segment inhomogeneous images. The

local image information is associated with the intensity difference between the average of local intensity distribution

and the original image,which can significantly increase the contrast between foreground and background.

Thus, the images with intensity inhomogeneity can be efficiently segmented. What is more, to avoid the reinitialization

of the level set function and shorten the computational time, a simple and fast level set evolution

formulation is used in the numerical implementation. Experimental results on synthetic images as well as real

medical images are shown in the paper to demonstrate the efficiency and robustness of the proposed method