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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Florin, C., Paragios, N., & Williams, J. (2005). Particle filters, a quasi-monte-carlo-solution for segmentation of coronaries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3749 LNCS, pp. 246–253). https://doi.org/10.1007/11566465_31
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