In order to solve the issue that traditional active contour models cannot quickly, accurately and robustly segment inhomogeneous intensity images, a hybrid active contour model combining bias field estimation and image segmentation is proposed. Firstly, through fuzzy clustering analysis for images, a bias field estimation model with the fuzzy membership function is proposed, which improves the ability to estimate and extract image intensity. Secondly, an adaptive scaling operator (ASO) is defined based on image information entropy, which improves segmentation efficiency and robustness to initialization and to noise. Finally, a hybrid active contour model is proposed by incorporating the bias field estimation model and the ASO into an energy functional. The final experiment results show that the proposed method not only has strong robustness to initialization and noise, but also has higher segmentation accuracy and segmentation efficiency for different degrees of inhomogeneous intensity images.