Enhancement, tracking, and analysis of digital angiograms.

Abstract

This dissertation presents image processing methods designed to enhance images obtained by angiography, and applied image analysis methods to quantify the vascular diameter. An iterative, non-linear enhancement technique is described for enhancing the edges of blood vessels in unsubtracted angiographic images. The technique uses a median filter and the point spread function of the imaging system to increase the resolution of the image while keeping down noise. Evaluation of the images by radiologists showed that they preferred the processed images over the unprocessed images. Also described is a heuristic, recursive, vessel tracking algorithm. The tracker is intended for use with digital subtraction angiography images. The vascular system is characterized by a tree data structure. Tree structures are inherently recursive structures and thus recursive programming languages are ideally suited for building and describing them. The tracker uses a window to follow the centerlines of the vessels and stores parameters describing the vessels in nodes of a binary tree. Branching of the vascular tree is handled automatically. A least squares fit of a cylindrical model to intensity profiles of the vessel is used to estimate vessel diameter and other parameters. The tracker is able to successfully track vessels with signal-to-noise ratios down to about 4. Several criteria are applied to distinguish between vessel and noise. The relative accuracy of the diameter estimate is about 3% to 8% for a signal-to-noise ratio of 10; the absolute accuracy depends on the magnification (mm per sample). For the clinically significant case of a 25% stenosis (narrowing of the vessel), the absolute error in estimating the percent stenosis is 3.7% of the normal diameter and the relative error is 14.8%. This relative error of 14.8% is a substantial improvement over relative errors of 30% to 70% produced by other methods.