Vessel extraction using anisotropic minimal paths and path score
Da Chen, Laurent David Cohen, Jean‐Marie Mirebeau · 2014
Geodesic methods have been widely applied to image analysis [1]. They are particularly efficient to extract a tubular structure, such as a blood vessel, given its two endpoints in a 2D or 3D medical image [2]. We address here a more difficult problem: the extraction of a full vessel tree structure given a single initial root, by growing a collection of keypoints, connected by geodesic minimal paths as in [3]. Keypoints are iteratively added, using selection criteria which compare geodesic distances with the standard euclidean curve length and a path score. A weakness of existing approaches is that the geodesic length and the euclidean path length are locally proportional, due to the use of an isotropic geodesic potential P(x). In contrast, we use an anisotropic geodesic potential P(x, v), and develop new criteria for selecting keypoints and stopping the tree growth. Experimental results demonstrate that our method can extract vessel structures at a finer scale, with increased accuracy.