This paper presents a new method to extract a network of vessels centerlines from a medical
image. The network is composed of local geodesics over a four-dimensional space that includes
local orientation and scale. These shortest paths follow closely the center of tubular structures
and can deal robustly with crossings over the image plane. The vessel network is grown
by an iterative algorithm that distributes seed points according to a geodesic saliency field.
Numerical experiments on a database of synthetic and medical images show the superiority
of our approach with respect to several methods based on shortest paths extractions. With
a minimum of user interaction, it allows to compute a complex network of vessels over noisy
medical.