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Predictive camera tracking for bronchoscope simulation with CONDensation.

Fani Deligianni·Adrian Chung·Guang-Zhong Yang
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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 multimodal 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.

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AlgorithmsArtifactsArtificial IntelligenceBronchoscopyComputer SimulationHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalModels, BiologicalModels, StatisticalPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySurgery, Computer-AssistedUser-Computer InterfaceJournal Article

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Fani Deligianni, Adrian Chung, Guang-Zhong Yang. 2005. Predictive camera tracking for bronchoscope simulation with CONDensation.. https://doi.org/10.1007/11566465_112

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