Probabilistic modeling based vessel enhancement in thoracic CT scans

Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications with particular importance for automated nodule detection. Ideally, vessel enhancement filters should enhance vessels and vessel junctions while suppressing nodules and other non-vessel elements. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the curvature tensor which is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing vessel enhancement filters are incapable of distinguishing between nodules and vessel junctions. In this paper we propose probabilistic vessel models from which novel vessel enhancement filters capable of enhancing junctions while suppressing nodules are derived. The proposed filters are based on eigenvalue analysis of the structure tensor which is a first order differential quantity and so are less sensitive to noise. The proposed filters are evaluated and compared to known techniques based on actual clinical data.