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 identifying said vessel structure in said 3D volume dataset and automatically skeletonizing said vessel structure to obtain a three-dimensional course of skeletonization paths, each having a skeletonization path length; in said evaluation device, along the skeletonization path of each skeletonization path, automatically electronically determining multiple characteristic quantities of said vessel structure as features relevant for an anomaly and forming at least one determined feature series from said features that represents said features along the skeletonization path length of that skeletonization path; storing respective reference feature series for each of different classes of known vessel structures each having a vessel structure length, said known vessel structures comprising known vessel structures with different anomalies and known vessel structures without anomalies, each reference feature series comprising multiple characteristic quantities that represent the known vessel structure along the vessel structure length of that known vessel structure; non-linearly imaging said at least one determined feature series to obtain a non-linear feature series, and automatically electronically matching said non-linear feature series to a closest one of said reference feature series with length, due to said non-linear imaging, being removed as a factor that would otherwise prevent a match to said closest one of said reference feature series; designating anomalies, if present, in the skeletonization path associated with said at least one determined feature series as corresponding to anomalies, if present, in said closest one of said reference feature series; and displaying an image of said vessel structure with said anomaly, if present, designated in said image.