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2006
IEEE

Pulsative Flow Segmentation in MRA Image Series by AR Modeling and EM Algorithm

12 years 28 days ago
Pulsative Flow Segmentation in MRA Image Series by AR Modeling and EM Algorithm
Segmentation of CSF and pulsative blood flow, based on a single phase contrast MRA (PC-MRA) image can lead to imperfect classifications. In this paper, we present a novel automated flow segmentation method by using PC-MRA image series. The intensity time series of each pixel is modeled as an autoregressive (AR) process and features including the Linear Prediction Coefficients (LPC), covariance matrix of LPC and variance of prediction error are extracted from each profile. Bayesian classification of the feature space is then achieved using a non-Gaussian likelihood probability function and unknown parameters of the likelihood function are estimated by a generalized ExpectationMaximization (EM) algorithm. The efficiency of the method evaluated on both synthetic and real retrospective gated PC-MRA images indicate that robust segmentation of CSF and vessels can be achieved by using this method.
Ali Gooya, Hongen Liao, Kiyoshi Matsumiya, Ken Mas
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where MIAR
Authors Ali Gooya, Hongen Liao, Kiyoshi Matsumiya, Ken Masamune, Takeyoshi Dohi
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