Integrated-model-based approach in recognizing blood vessels in MR images

Abstract

An integrated model-based approach for extracting blood vessels in MR images is presented. A Generalized Stochastic Tube model is used to capture both the shape of local tube-like object segments and the shape dynamics of global object trajectories. The blood flow within cross sections is explicitly modeled using a bivariate Gaussian density function that predicts the expected sensor measurement configuration. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that integrating both shape and blood flow models yields accurate and robust performance even under noisy conditions.