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17 February 2012 Robust and efficient fiducial tracking for augmented reality in HD-laparoscopic video streams
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Abstract
Augmented Reality (AR) is a convenient way of porting information from medical images into the surgical field of view and can deliver valuable assistance to the surgeon, especially in laparoscopic procedures. In addition, high definition (HD) laparoscopic video devices are a great improvement over the previously used low resolution equipment. However, in AR applications that rely on real-time detection of fiducials from video streams, the demand for efficient image processing has increased due to the introduction of HD devices. We present an algorithm based on the well-known Conditional Density Propagation (CONDENSATION) algorithm which can satisfy these new demands. By incorporating a prediction around an already existing and robust segmentation algorithm, we can speed up the whole procedure while leaving the robustness of the fiducial segmentation untouched. For evaluation purposes we tested the algorithm on recordings from real interventions, allowing for a meaningful interpretation of the results. Our results show that we can accelerate the segmentation by a factor of 3.5 on average. Moreover, the prediction information can be used to compensate for fiducials that are temporarily occluded or out of scope, providing greater stability.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. Mueller, A. Groch, M. Baumhauer, L. Maier-Hein, D. Teber, J. Rassweiler, H.-P. Meinzer, and In. Wegner "Robust and efficient fiducial tracking for augmented reality in HD-laparoscopic video streams", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83161M (17 February 2012); https://doi.org/10.1117/12.911993
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