Modern volumetric imaging techniques such as CT or MRI, aid in the understanding of a patient's anatomy and
pathologies. Depending on the medical use case, various anatomical structures are of interest. Blood vessels
play an important role in several applications, e.g. surgical planning. Manual delineation of blood vessels in
volumetric images is error prone and time consuming. Automated vessel segmentation is a challenging problem
due to acquisition-dependent problems such as noise, contrast, spatial resolution, and artifacts. In this paper, a
vessel segmentation method is presented that combines a wavefront propagation technique with Hessian-based
vessel enhancement. The latter has proven its usefulness as a preprocessing step to detect tubular structures
before the actual segmentation is carried out. The former allows for an ordered growing process, which enables
topological analysis. The contribution of this work is as follows. 1. A new vessel enhancement filter for tubular
structures based on the Laplacian is proposed, 2. a wavefront propagation technique is proposed that prevents
leaks by imposing a threshold on the maximum number of voxels that the propagating front must contain, and 3.
a volumetric hole filling method is proposed to filll holes, bays, and tunnels which are caused at locations where
the tubular structure assumption is violated. The proposed method reduces approximately 50% of the necessary
eigenvalue calculations for vessel enhancement and prevents leaks starting at small spots, which usually occur
using standard region growing. Qualitative and quantitative evaluation based on several metrics (statistical
measures, dice and symmetric average surface distance) is presented.