An accurate analysis of the spatial distribution of blood flow in any organ must be based on detailed morphometry (diameters, lengths, vessel numbers, and branching pattern) of the organ vasculature. Despite the significance of detailed morphometric data, there is relative scarcity of data on 3D vascular anatomy. One of the major reasons is that the process of morphometric data collection is labor intensive. The objective of this study is to validate a novel segmentation algorithm for semi-automation of morphometric data extraction. The utility of the method is demonstrated in porcine coronary arteries imaged by computerized tomography (CT). The coronary arteries of five porcine hearts were injected with a contrast-enhancing polymer. The coronary arterial tree proximal to 1 mm was extracted from the 3D CT images. By determining the centerlines of the extracted vessels, the vessel radii and lengths were identified for various vessel segments. The extraction algorithm described in this paper is based on a topological analysis of a vector field generated by normal vectors of the extracted vessel wall. With this approach, special focus is placed on achieving the highest accuracy of the measured values. To validate the algorithm, the results were compared to optical measurements of the main trunk of the coronary arteries with microscopy. The agreement was found to be excellent with a root mean square deviation between computed vessel diameters and optical measurements of 0.16 mm (<10% of the mean value) and an average deviation of 0.08 mm. The utility and future applications of the proposed method to speed up morphometric measurements of vascular trees are discussed.