Cerebrovascular segmentation by accurate probabilistic modeling of TOF-MRA images

Authors:
Ayman El-Baz;Aly Farag;Georgy Gimelfarb
Affiliations:
Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, Auckland, New Zealand
Venue:
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Year:
2005

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Abstract

We present a fast algorithm for automatic extraction of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data. Blood vessels are separated from background tissues (fat, bones, or grey and white brain matter) by voxel-wise classification based on precise approximation of a multi-modal empirical marginal intensity distribution of the TOF-MRA data. The approximation involves a linear combination of discrete Gaussians (LCDG) with alternating signs, and we modify the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.