Stochastic Segmentation of Blood Vessels From Time-of-Flight

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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.

M. Sabry Hassouna

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Blood Vessel Segmentation | Expectation Mximization (EM) Algorithm | Magnetic Resonance Angiography (MRA) | Markov Random Fields (MRF) | Statistical Segmentation | Time-of-flight (TOF) |

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Added	09 Nov 2008
Updated	12 Nov 2008
Authors	M. Sabry Hassouna

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Stochastic Segmentation of Blood Vessels From Time-of-Flight

Blood Vessel Segmentation | Expectation Mximization (EM) Algorithm | Magnetic Resonance Angiography (MRA) | Markov Random Fields (MRF) | Statistical Segmentation | Time-of-flight (TOF) |

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