Active contour driven by region-scalable fitting and Kullback-Leibler divergence for image segmentation

To overcome the problem of high computational cost of active contour model, a new local region-based active contour model in a variational level set formulation for image segmentation is proposed. An energy function based on the region-scalable fitting (RSF) term and the Kullback-Leibler divergence term is formulated. The existing methods construct the energy function for segmentation through computing the distances among the intraregion points and the ″center″ fitting this region, representing similarity of object region. An energy term including the disparity measured by Kullback-Leibler divergence between regions to be segmented is added to the energy function of the RSF model in the proposed model. The model can handle blurry boundaries and noise problems. The proposed method is applied to segment synthetic and real images, and the experimental results show that KL-RSF can improve the effectiveness of segmentation while ensuring the accuracy through accelerating the minimization of the energy function.