Cerebrovascular Segmentation by Accurate Probabilistic Modeling of TOF-MRA Images

  • Ayman El-Baz
  • Aly Farag
  • Georgy Gimelfarb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ayman El-Baz
    • 1
  • Aly Farag
    • 1
  • Georgy Gimelfarb
    • 2
  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisvilleUSA
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

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