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1 March 2011 On scale invariant features and sequential Monte Carlo sampling for bronchoscope tracking
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Abstract
This paper presents an improved bronchoscope tracking method for bronchoscopic navigation using scale invariant features and sequential Monte Carlo sampling. Although image-based methods are widely discussed in the community of bronchoscope tracking, they are still limited to characteristic information such as bronchial bifurcations or folds and cannot automatically resume the tracking procedure after failures, which result usually from problematic bronchoscopic video frames or airway deformation. To overcome these problems, we propose a new approach that integrates scale invariant feature-based camera motion estimation into sequential Monte Carlo sampling to achieve an accurate and robust tracking. In our approach, sequential Monte Carlo sampling is employed to recursively estimate the posterior probability densities of the bronchoscope camera motion parameters according to the observation model based on scale invariant feature-based camera motion recovery. We evaluate our proposed method on patient datasets. Experimental results illustrate that our proposed method can track a bronchoscope more accurate and robust than current state-of-the-art method, particularly increasing the tracking performance by 38.7% without using an additional position sensor.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xióngbiao Luó, Marco Feuerstein, Takayuki Kitasaka, Hiroshi Natori, Hirotsugu Takabatake, Yoshinori Hasegawa, and Kensaku Mori "On scale invariant features and sequential Monte Carlo sampling for bronchoscope tracking", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79640Q (1 March 2011); https://doi.org/10.1117/12.877515
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