ESTIMATION OF BLOOD VESSEL BOUNDARIES IN X-RAY IMAGES.

A new approach to blood vessel boundary estimation is presented. By modeling the blood vessel as a dynamically evolving state vector, and by taking into account the Poisson statistics of the x-ray imaging noise, a state-space system is obtained with a nonlinear measurement equation which includes non-Gaussian, nonadditive noise. Maximum a posteriori smoothing equations are derived for the state vector describing the vessel, and the optimally smoothed state vector is found by a dynamic programming search. This method performs especially well in images with low snr and low sampling rate. The performance of the proposed method is demonstrated by the boundary estimates obtained by applying the algorithm to a simulated vessel and measurement data as well as to real vessel phantom measurement data at various snrs.