GAP FILLING IN 3D VESSEL LIKE PATTERNS WITH TENSOR FIELDS: Application to High Resolution Computed Tomography Images of Vessel Networks

摘要

We present an algorithm for merging discontinuities in three-dimensional (3D) images of tubular structures. The application of the proposed method is associated with large 3D images presenting undesirable discontinuities. In order to recover the real network topology, we need to fill the gap between the closest discontinuous tubular segments. We present a new algorithm to achieve this goal based on a tensor voting method. This algorithm is robust, relatively fast and does not require numerous parameters nor manual intervention. Representative results are illustrated on real 3D micro-vascular networks.