Visual marker based tracking is one of the most widely used tracking techniques in Augmented Reality (AR) applications. Generally, multiple square markers are needed to perform robust and accurate tracking. Various marker
based methods for calibrating relative marker poses have already been proposed. However, the calibration accuracy of
these methods relies on the order of the image sequence and pre-evaluation of pose-estimation errors, making the method offline. Several studies have shown that the accuracy of pose estimation for an individual square marker depends on camera distance and viewing angle. We propose a method to accurately model the error in the estimated pose and translation of a camera using a single marker via an online method based on the Scaled Unscented Transform (SUT). Thus, the pose estimation for each marker can be estimated with highly accurate calibration results independent of the order of image sequences compared to cases when this knowledge is not used. This removes the need for having multiple markers and an offline estimation system to calculate camera pose in an AR application.
Motivation: The existing visualization of the Camera augmented mobile C-arm (CamC) system does not have enough cues for depth information and presents the anatomical information in a confusing way to surgeons. Methods: We propose a method that segments anatomical information from X-ray and then augment it on the video images. To provide depth cues, pixels belonging to video images are classified as skin and object classes. The augmentation of anatomical information from X-ray is performed only when pixels have a larger probability of belonging to skin class. Results: We tested our algorithm by displaying the new visualization to 2 expert surgeons and 1 medical student during three surgical workflow sequences of the interlocking of intramedullary nail procedure, namely: skin incision, center punching, and drilling. Via a survey questionnaire, they were asked to assess the new visualization when compared to the current alphablending overlay image displayed by CamC. The participants all agreed (100%) that occlusion and instrument tip position detection were immediately improved with our technique. When asked if our visualization has potential to replace the existing alpha-blending overlay during interlocking procedures, all participants did not hesitate to suggest an immediate integration of the visualization for the correct navigation and guidance of the procedure. Conclusion: Current alpha blending visualizations lack proper depth cues and can be a source of confusion for the surgeons when performing surgery. Our visualization concept shows great potential in alleviating occlusion and facilitating clinician understanding during specific workflow steps of the intramedullary nailing procedure.