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On pattern selection for laparoscope calibration

Research output: Contribution to journalConference paper

Stephen Thompson, Yannic Meurer, Eddie Edwards, João Ramalhinho, Maria Ruxandra Robu, Danail Stoyanov, Sébastien Ourselin, Brian Davidson, David Hawkes, Matthew J. Clarkson

Original languageEnglish
Publication statusPublished - 1 Jan 2017
EventMedical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling - Orlando, United States
Duration: 14 Feb 201716 Feb 2017

King's Authors


Camera calibration is a key requirement for augmented reality in surgery. Calibration of laparoscopes provides two challenges that are not sufficiently addressed in the literature. In the case of stereo laparoscopes the small distance (less than 5mm) between the channels means that the calibration pattern is an order of magnitude more distant than the stereo separation. For laparoscopes in general, if an external tracking system is used, hand-eye calibration is difficult due to the long length of the laparoscope. Laparoscope intrinsic, stereo and hand-eye calibration all rely on accurate feature point selection and accurate estimation of the camera pose with respect to a calibration pattern. We compare 3 calibration patterns, chessboard, rings, and AprilTags. We measure the error in estimating the camera intrinsic parameters and the camera poses. Accuracy of camera pose estimation will determine the accuracy with which subsequent stereo or hand-eye calibration can be done. We compare the results of repeated real calibrations and simulations using idealised noise, to determine the expected accuracy of different methods and the sources of error. The results do indicate that feature detection based on rings is more accurate than a chessboard, however this doesn't necessarily lead to a better calibration. Using a grid with identifiable tags enables detection of features nearer the image boundary, which may improve calibration.

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