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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
El-Baz, A., Farag, A., & Gimelfarb, G. (2005). Cerebrovascular segmentation by accurate probabilistic modeling of TOF-MRA images. In Lecture Notes in Computer Science (Vol. 3540, pp. 1128–1137). Springer Verlag. https://doi.org/10.1007/11499145_114
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