Vascular bifurcation detection in scale-space

Several methods have been proposed over the years for segmentation of vessels, many of them based on scale-space. However, none of the existing methods for blood vessel segmentation is appropriate for extension to bifurcation detection. Other existing bifurcation detection algorithms use an inherently serial “track and detect” approach, which also requires a seed point. We present for the first time a comprehensive scale-space analysis of vascular bifurcations, resulting in a simple, novel algorithm for direct detection of blood vessel bifurcation points based not only on spatial variation across scales, but also on the variation at a single spatial point across scales, without requiring training data or seed points. We present an analytical model for the bifurcation evolution with increasing scale, which was combined with eigenvalue analysis to create a bifurcation-Ness filter. We reveal, for the first time, a hybrid structure of bifurcations in scale-space. The algorithm was tested for validation in both 2D and 3D, with synthetic data as well as medical and non-medical images.