This paper presents an image processing method for information extraction from 3D images of vasculature. It automates the study of vascular structures by extracting quantitative information such as skeleton, length, diameter, and vessel-to- tissue ratio for different vessels as well as their branches. Furthermore, it generates 3D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3D connectivity. Steps of the proposed approach are as follows. (1) Preprocessing, in which intensity adjustment, optimal thresholding, and median filtering are done. (2) 3D thinning, in which medial axis and skeleton of the vessels are found. (3) Branch labeling, in which different branches are identified and each voxel is assigned to the corresponding branch. (4) Quantitation, in which length of each branch is estimated, based on the number of voxels assigned to it, and its diameter is calculated using the medial axis direction. (5) Visualization, in which vascular structure is shown in 3D, using color coding and surface rendering methods. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.