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A novel adaptive B-Spline deformable model is presented in this paper for object segmentation. Comparing with other B-Spline models, the proposed model has the following advantages. First, a fully automatic and affine-invariant strategy is proposed for landmark point assignment. Second, contrary to other B-Spline models that rely on predetermined number of control points, an automatic scheme for control point insertion is designed to enhance the adaptivity and the flexibility of B-Spline model for segmenting shapes with high complexity. Thirdly, a statistical framework is embedded for modeling the shape distribution and appearance characteristics of landmark points in the training samples. Fine deformation can be achieved through the minimum mean square error approach that allows the model to accurately adapt to the desired object boundaries in the image. Experiments on medical image segmentation are carried out to validate the performance, and comparison has been made with respect to the traditional Snake and ASM. It turns out that the proposed adaptive B-Spline model can attain more accurate object segmentation. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]