Abstract (EN)

We propose a new dynamic Riemannian metric with adaptive anisotropy enhancement and with appearance feature coherence penalization. The appearance features are characterized by the orientation score maps. Unlike the static geodesic metrics which depend on local pointwise information, the dynamic metric can take into account the nonlocal feature coherence penalty in order to extract a desired structure from complicated background or from a vessel tree. We construct the metric using the information from two external reference points which are identified during the geodesic distance computation. Numerical experiments are performed in retinal vessels, including the independent results from the proposed dynamic metric itself and the comparison against existing minimal path models. The results show that the proposed metric indeed gets better performance than state-of-the-art geodesic metrics.