A new active contour model based on global and local image information is proposed for image segmentation. The energy functional for the proposed model consists of three terms

i.e.

the global intensity fitting term

the local intensity fitting term

and the adaptive parameter term. The global intensity fitting term incorporates global image information and the local intensity fitting term uses local contextual information. The weighting factor between the global and local intensity fitting term is adaptive by the image content.By incorporating the local and global image information into the proposed model

the images can be efficiently segmented. In addition

convex optimization is added to the new model to get the global minima. Finally

the Split-Bregman method can effectively improve the segmentation speed. Experimental results demonstrate that the proposed algorithm is robust to the choice of initialization values

can get the more accurate segmentation result

and especially is about 1.5 to 2 times faster than the C-V (Chan & Vese) model.