The present paper focuses on the conversion of multidimensional image structures to art object-centered, abstract description encoding shape features and structure relationships. We describe a prototype system that extracts three-dimensional (3-D) curvilinear structures from volume image data and transforms them into a symbofic description which represents topological and geometrical features of tree-fike, filamentous objects. The initial segmentation is performed by 3-D hysteresis thresholding. A skeletal structure is derived by 3-D binary thinning, approximating the center-fines while fully preserving the 3-D topology. The local width of the fine structures is characterized by a separate 3-D Eucfidean distance transform. Compilation, or raster-to-vector transformation, converts the maximally thinned voxel fists into a vector description. The final graph data-structure encodes the spatial course of fine sections, the estimate of the local diameter, and the topology at important key locations fike branchings and end-points. The analysis system is appfied to the characterization of the cerebral vascular system segmented from magnetic resonance angiography (MRA).