Geodesic active regions using non-parametric statistical regional description and their application to aneurysm segmentation from CTA

The inclusion of statistical region-based information in the Geodesic Active Contours introduces robustness in the segmentation of images with weak or inhomogeneous gradient at edges. The estimation of the Probability Density Function (PDF) for each region, involves the definition of the features that characterize the image inside the different regions. PDFs are usually modelled from the intensity values using Gaussian Mixture Models. However, we argue that the use of up to second order information could provide better discrimination of the different regions than based on intensity only, as the local intensity manifold is more accurately represented. In this paper, we present a non parametric estimation technique for the PDFs of the underlying tissues present in medical images with application for the segmentation of brain aneurysms in CTA data with the Geodesic Active Regions model.