Characterization of Vasculature in 3D Medical Images
Noninvasive determination of blood vessel boundaries for accurate characterization of vascular detail is an important, clinically relevant problem. In two-dimensional medical images of vascular structures such as conventional or digital subtraction angiograms, it is often difficult to identify and evaluate the extent of vascular stenosis in tortuous vessels, at vessel branching points, and when vessels overlap or cross over other vessels. Thus, it is of clinical importance to be able to accurately map out and track vessels in three dimensions. With current diagnostic imaging modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), true three-dimensional images are becoming available. An algorithm for tracking vessels in 3-D images has been developed in order to utilize all the information contained in such data sets. The algorithm is generic, hierarchical, and modular, and consists of a low level processor for 3-D boundary point detection, an intermediate level module for linking the detected boundary points into complete object surfaces, and a high level component for further identification and description of objects in the image. The algorithm has been tested and evaluated using simulated images and its robustness has been demonstrated. Results obtained with a real MRI 3-D data set are presented. The inclusion of a 3-D display of vasculature along with the quantitative parameters of position (centroid) and degree of stenosis (via the vessel radius measure) as computed here, could greatly facilitate assessment of cardiovascular structure and function.
KeywordsHierarchical Algorithm Digital Subtraction Angiogram Tracking Vessel High Level Component Boundary Voxels
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