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Inferring Vascular Structure from 2D and 3D Imagery

  • Abhir Bhalerao
  • Elke Thönnes
  • Wilfrid Kendall
  • Roland Wilson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2208)

Abstract

We describe a method for inferring vascular (tree-like) structures from 2D and 3D imagery. A Bayesian formulation is used to make effective use of prior knowledge of likely tree structures with the observed being modelled locally with intensity profiles as being Gaussian. The local feature models are estimated by combination of a multiresolution, windowed Fourier approach followed by an iterative, minimum meansquare estimation, which is both computationally efficient and robust. A Markov Chain Monte Carlo (MCMC) algorithm is employed to produce approximate samples from the posterior distribution given the feature model estimates. We present results of the multiresolution parameter estimation on representative 2D and 3D data, and show preliminary results of our implementation of the MCMC algorithm 1.

Keywords

Posterior Distribution Markov Chain Monte Carlo Block Size Global Structure Markov Chain Monte Carlo Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Abhir Bhalerao
    • 1
  • Elke Thönnes
    • 2
  • Wilfrid Kendall
    • 2
  • Roland Wilson
    • 1
  1. 1.Department of Computer ScienceUniversity of WarwickUK
  2. 2.Department of StatisticsUniversity of WarwickUK

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