  The identification of vascular networks is an important topic in the medical
image analysis community. While most methods focus on single vessel tracking,
the few solutions that exist for tracking complete vascular networks are
usually computationally intensive and require a lot of user interaction. In
this paper we present a method to track full vascular networks iteratively
using a single starting point. Our approach is based on a cloud of sampling
points distributed over concentric spherical layers. We also proposed a vessel
model and a metric of how well a sample point fits this model. Then, we
implement the network tracking as a min-cost flow problem, and propose a novel
optimization scheme to iteratively track the vessel structure by inherently
handling bifurcations and paths. The method was tested using both synthetic and
real images. On the 9 different data-sets of synthetic blood vessels, we
achieved maximum accuracies of more than 98\%. We further use the synthetic
data-set to analyse the sensibility of our method to parameter setting, showing
the robustness of the proposed algorithm. For real images, we used coronary,
carotid and pulmonary data to segment vascular structures and present the
visual results. Still for real images, we present numerical and visual results
for networks of nerve fibers in the olfactory system. Further visual results
also show the potential of our approach for identifying vascular networks
topologies. The presented method delivers good results for the several
different datasets tested and have potential for segmenting vessel-like
structures. Also, the topology information, inherently extracted, can be used
for further analysis to computed aided diagnosis and surgical planning.
Finally, the method's modular aspect holds potential for problem-oriented
adjustments and improvements.
