In this paper we propose a new supervised active contour model evolving with Haralick texture features. This model is divided in two stages. First, we use a supervised step where the user defines an ideal segmentation on a learning image. A linear programming model, modeling the behavior of the active contour, is then used to determine the weights of the Haralick features leading to the optimal segmentation. In a second step, a texture-oriented active contour based on the Chan-Vese model is launched on several test images with the learned weights and the closest segmentations to the one defined on the learning image is determined. Results of our method are presented on medical echographic images.
