Aimed at the problem of the traditional region-based active contour model, which was difficult to segment the noisy images and intensity inhomogeneous images, an active contour model, including global term, local term and regularization term, was proposed. Global term was derived from the data fidelity items of ChanVese model. Meanwhile, the construction of local term took the local intensity information into account and the image local entropy that reflected the grey characteristics was introduced. According to the characteristics of different images, the reasonable parameters of global item and local item were selected. The regularization term was added to ensure the smoothness of curve evolution and to guarantee the reliability of segmentation. A variable level set was used to minimize the energy function to get the gradient descent flow. Experimental results of synthetic images and real images demonstrate that the proposed approach is efficient in segmenting the noisy images and inhomogeneity images.