  The minimal path method has proven to be particularly useful and efficient in
tubular structure segmentation applications. In this paper, we propose a new
minimal path model associated with a dynamic Riemannian metric embedded with an
appearance feature coherence penalty and an adaptive anisotropy enhancement
term. The features that characterize the appearance and anisotropy properties
of a tubular structure are extracted through the associated orientation score.
The proposed dynamic Riemannian metric is updated in the course of the geodesic
distance computation carried out by the efficient single-pass fast marching
method. Compared to state-of-the-art minimal path models, the proposed minimal
path model is able to extract the desired tubular structures from a complicated
vessel tree structure. In addition, we propose an efficient prior path-based
method to search for vessel radius value at each centerline position of the
target. Finally, we perform the numerical experiments on both synthetic and
real images. The quantitive validation is carried out on retinal vessel images.
The results indicate that the proposed model indeed achieves a promising
performance.
