Cerebrovascular Segmentation from TOF Using Stochastic Models

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, while the high level process characterizes their statistical dependence among neighboring voxels. The low level process of the background signal is modeled by a finite mixture of one Rayleigh and two normal distributions, while the blood vessels are modeled by one normal distribution. The parameters of the low level process are estimated using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the model parameters, an automatic method for parameter initialization, based on histogram analysis, is provided. To improve the quality of segmentat...

• Type: Journal
• Year: 2006
• Where: MEDIA (Medical Image Analysis)
• Authors: M. Sabry Hassouna, Aly A. Farag, Stephen Hushek, Thomas Moriarty
• Attachments: 1 file(s)
• Keywords: Blood Vessel Segmentation, Expectation Mximization (EM) Algorithm, Magnetic Resonance Angiography (MRA), Markov Random Fields (MRF), Statistical Segmentation, Time-of-flight (TOF)