Stochastic segmentation of blood vessels from time-of-flight
In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data across the volume, while the high level process characterizes the statistical dependence among neighboring voxels. 3D Markov random field (MRF) has been employed to model the high level process, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE). Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several TOF datasets. Experimental results showed that the proposed model is capable of delineating vessels down to 3 voxel diameters.