System for 3D visualization and data mining of large vascular trees

Modern micro-CT scanners produce very large 3D digital images of arterial trees. A typical 3D micro-CT image can consist of several hundred megabytes of image data, with a voxel resolution on the order of ten microns. The analysis and subsequent visualization of such images poses a considerable challenge. We describe a computerbased system for analyzing and visualizing such large 3D data sets. The system, dubbed the Tree Analyzer, processes an image in four major stages. In the first two stages, a series of automated 3D image-processing operations are applied to an input 3D digital image to produce a raw arterial tree and several supplemental data structures describing the tree (central-axis structure, surface rendering polygonal data, quantitative description of all tree branches). Next, the human interacts with the system to visualize and correct potential defects in the extracted raw tree. A series of sophisticated 3D editing tools and automated operations are available for this step. Finally, the corrected tree can be visualized and manipulated for data mining, using a large number of graphics-based rendering tools, such as 3D stereo viewing, global and local surface rendering, sliding-thin slabs, multiplanar reformatted views, projection images, and an interactive tree map. Quantitative data can also be perused for the tree. Results are presented for 3D micro-CT images of the heart and liver.