Vascular structures such as the pulmonary arterial tree contain hundreds of thousands of vessel segments, making structural and functional analysis of an entire 3D image volume very difficult. Currently-available methods for segmentation and morphometry of 3D vascular tree images require user interaction making the task very tedious and sometimes impossible. Our aim is to exploit the self-similar nature of arterial trees to simplify morphometric analysis. The structure of pulmonary arterial trees exhibits self- similarity in the sense that the segment length and diameter data from different pathways are statistically indistinguishable for subtrees distal to a given segment diameter. We analyze 3D micro-CT images of mouse and rat pulmonary arterial trees by measuring the lengths and diameters of the vessel segments of the several longest arterial pathways and their immediate branches interactively. Since measurements made on the longest pathways are representative of the tree as a whole, and there are less than 30 branches off the main trunk, the morphometry of the complex tree can be characterized by less than 100 length and diameter measurements.
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