Detecting tubular structures via direct vector field singularity characterization

The initial step of vessel segmentation in 3D is the detection of vessel centerlines. The proposed methods in literature are either dependent on vessel radius and/or have low response at vessel bifurcations. In this paper we propose a 3D tubular structure detection method that removes these two drawbacks. The proposed method exploits the observations on the eigenvalues of the Hessian matrix as is done in literature, yet it employs a direct 3D vector field singularity characterization. The Gradient Vector Flow vector field is used and the eigenvalues of its Jacobian are exploited in computing a parameter free vesselness map. Results on phantom and real patient data exhibit robustness to scale, high response at vessel bifurcations, and good noise/non-vessel structure suppression.