Estimation Of Blood Vessel Boundaries In X-Ray Images

Abstract

A new approach to blood vessel boundary estimation is presented in this paper. By appropriately modelling the blood vessel as a dynamically evolving state vector, and by taking into account the Poisson statistics of the x-ray imaging noise, we arrive at a state-space system with a non-linear measurement equation which includes non-Gaussian, non-additive noise. MAP 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 SNR's.