Design and study of flux-based features for 3D vascular tracking

In this paper, we present and study two local features for the tracking of vascular structures on 3D angiograms. The first one, Flux, measures the inward gradient flux through circular cross-sections. The second one, MFlux, introduces a non-linear penalization of asymmetric flux contributions to reduce false positive responses. Through a series of experiments on synthetic and real cardiac CT data, we discuss the properties of these features with respect to their parameters. We compare them to a selection of published vessel- dedicated features. We show that MFlux induces a particularly discriminative response landscape, which is a desirable property for tracking purposes on such large search spaces. A key characteristic of the proposed features is their simplicity of implementation and their high computational efficiency, enabling their practical use for advanced tracking strategies.