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Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries

  • Charles Florin
  • Nikos Paragios
  • Jim Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3749)

Abstract

In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.

Keywords

Gaussian Mixture Model Particle Filter Sampling Importance Successive Plane Vessel Segmentation 
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 2005

Authors and Affiliations

  • Charles Florin
    • 1
  • Nikos Paragios
    • 2
  • Jim Williams
    • 1
  1. 1.Imaging & Visualization DepartmentSiemens Corporate ResearchPrincetonUSA
  2. 2.CERTIS – Ecole Nationale des Ponts et ChausseesChamps-sur-MarneFrance

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