Predictive camera tracking for bronchoscope simulation with CONDensation

Authors:
Fani Deligianni;Adrian Chung;Guang Zhong
Affiliations:
Department of Computing, Imperial College London;Department of Computing, Imperial College London;Department of Computing, Imperial College London
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

Citing 4
Cited 4

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Abstract

This paper exploits the use of temporal information to minimize the ambiguity of camera motion tracking in bronchoscope simulation. The condensation algorithm (Sequential Monte Carlo) has been used to propagate the probability distribution of the state space. For motion prediction, a second-order auto-regressive model has been used to characterize camera motion in a bounded lumen as encountered in bronchoscope examination. The method caters for multi-modal probability distributions, and experimental results from both phantom and patient data demonstrate a significant improvement in tracking accuracy especially in cases where there is airway deformation and image artefacts.