Particle filters, a quasi-monte carlo solution for segmentation of coronaries

Authors:
Charles Florin;Nikos Paragios;Jim Williams
Affiliations:
Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ;CERTIS – Ecole Nationale des Ponts et Chaussees, Champs-sur-Marne, France;Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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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.