Alpha-divergence maximization for statistical region-based active contour segmentation with non-parametric PDF estimations
In this article, a complete original framework for unsupervised statistical region-based active contour segmentation is proposed. More precisely, the method is based on the maximization of alpha-divergences between non-paramterically estimated probability density functions (PDFs) of the inner and outer regions defined by the evolving curve. We define the variational context associated to distance maximization in the particular case of alpha-divergences and provide the complete derivation of the partial differential equation leading the segmentation. Results on synthetic data, corrupted with a high level of Gaussian and Poisson noises, but also on clinical X-ray images show that the proposed unsupervised method improves standard approaches of that kind.