1. A method for fully automatically detecting anomalies in vessel structures, comprising the steps of: obtaining a 3D volume dataset representing an image of an in vivo vessel structure; supplying said 3D volume dataset to an electronic evaluation device and, in said electronic evaluation device, automatically detecting said vessel structure in said 3D volume dataset and automatically skeletonizing said vessel structure for obtaining a three-dimensional course of skeletonization paths; in said evaluation device, for each skeletonization path, automatically electronically determining characteristic quantities of said vessel structure as features relevant for an anomaly and forming at least one feature series from said features; storing reference feature series for different classes of known vessel structures, said known vessel structures comprising known vessel structures with different anomalies and known vessel structures without anomalies; non-linearly imaging said at least one feature series for obtaining a non-linear feature series, and automatically electronically matching said non-linear feature series to a closest one of said reference feature series; and designating anomalies, if present, in the skeletonization path associated with said at least one feature series as corresponding to anomalies, if present, in said closest one of said reference feature series.