The conventional method requires a centerline of a vessel to estimate the vessel diameter. Two methods of estimating the centerline of vessels have been reported: One is to manually define the centerline of the vessel. This potentially contributes to inter- and intra-observer variability. The orientation of the centerline has an effect on the diameter function since diameters are computed perpendicular to the centerline. And the other is to automatically detect the centerline of the vessel. But this is a very complicated method. In this paper, we propose a new method of estimating vessel diameter using direction codes without detecting centerline. Since this method detects the vessel boundary and direction code at the same time, it simplifies the procedure and reduces execution time in estimating the vessel diameter. Compared to a method that automatically estimates the vessel diameter using centerline, a proposed method provides an improved accuracy in image with poor contrast, branching or obstructed vessels. Also, this provides a good compression of boundary description. Our experiments demonstrate the usefulness of the technique using direction code for quantitative angiography. Experimental results justify the validity of the proposed method.

To derive blood flow dynamics from cineangiograms (CAG), we have developed an image processing algorithm to estimate a two-dimensional blood fiow velocity map projected on CAG. Each image area of CAG is diveded into blocks, and it is assumed that the movement of the contrast medium between two serial frames is restricted only to adjacent blocks. By this assumption, a fundamental equation" and the maximum flow constraints" are derived. The equation and constraints state the relationship between the volume of contrast medium in each block and the flow components" that are the volumes of contrast medium flowing from/to its adjacent blocks. The initial guess" that is a set of approximately obtained flow components is corrected using these relationships. The corrected flow components are then transformed into blood flow velocities, which are illustrated in the form of a needle diagram. In numerical experiments, the estimation error between the real flow velocity generated artificially and the flow velocity estimated with our algorithm was evaluated under one of the worst conditions. Although the maximum error was fairly large, the estimated flow velocity map was still acceptable for visual inspection of flow velocity pattern. We then applied our algorithm to an abdominal CAG (clinical data). The results showed flow stagnation and reverse flow in the abdominal aneurysm, which are consistent with the presence of a thrombus in the aneurysm. This algorithm may be a useful diagnostic tool in the assessment of vascular disease.