The abdominal aorta is the most common site for an aneurysm, which may lead to hemorrhage and death, to develop. The aim of this study was to develop a semi-automated method to de-lineate the vessels and detect the center-line of these vessels to make measurements necessary for stent design from multi-detector computed tomograms. We developed a robust method of tracking the aortic vessel tree with branches from a user selected seed point along the vessel path using scale space approaches, central transformation measures, vessel direction findings, iterative corrections and a priori information in determining the vessel branches. Fifteen patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with a 3.2 mm thickness, 1.5 mm slice spacing, and a stack of 512x512x320 volume data sets were reconstructed. The algorithm required an initial user input to locate the vessel seen in axial CT slice. Next, the automated image processing took approximately two minutes to compute the centerline and borders of the aortic vessel tree. The results between the manually and automatically generated vessel diameters were compared and statistics were computed. We observed our algorithm was consistent (less than 0.01 S.D) and similar (less than 0.1 S.D) to manual results.