Aiming at the problem that the traditional active contour model cannot accurately segment images with intensity inhomogeneity and is sensitive to scale parameters, an adaptive scale active contour model based on region information is proposed. The adaptive scale operator is constructed based on the local entropy of the image, and the energy function is constructed by using the local intensity clustering property of the image. A linear combination of a set of smooth basis functions is used to represent the bias field to increases the stability of the model. By minimizing this energy, the proposed model is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction. Experimental results show that, compared with the other four models, the proposal has higher segmentation accuracy, and the segmentation result is robust to the initial level set function and noise in the image.