Abstract
This paper presents a motion vector-based method to detect and remove the outlier of the matched feature point in laparoscopic images. Feature point detected on organ surface in laparoscopic images plays an important role not only in laparoscopic tracking but also in organ surface shape reconstruction. However, many factors such as the deformation of the organ or the movement of the surgical tools result to the outliers in matched feature points, thus the feature point based tracking and reconstruction will have larger errors. Traditional methods use these points either directly (inside a RANSAC scheme) or after a prior knowledge of compensation, which may lead to larger error in tracking and reconstruction. We introduce the motion vector (MV) based method to detect outliers among the matched feature points. MV is originally used in the compression of the video streams, we exploit it to detect the movement of one feature point in different video frames. The outliers of feature point can be detected by enforcing a direction constraint with its MV. Our method had been implement under a SLAM-based framework for laparoscopic tracking, we modified the map management of SLAM for better laparoscopic tracking. The experimental results showed that our method effectively detects and removes the outliers without any prior knowledge; the average precision rate in image pairs was 95.9\(\%\).
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Baumhauer, M., Feuerstein, M., Meinzer, H.P., Rassweiler, J.: Navigation in endoscopic soft tissue surgery: perspectives and limitations. J. Endourol. 22(4), 751–766 (2008)
Grasa, O.G., Bernal, E., Casado, S., Gil, I., Montiel, J.M.M.: Visual SLAM for handheld monocular endoscope. IEEE Trans. Med. Imaging 33(1), 135–146 (2014)
Civera, J., Grasa, O.G., Davison, A.J., Montiel, J.M.M.: 1-Point RANSAC for extended kalman filtering: application to real-time structure from motion and visual odometry. J. Field Robot. 27(5), 609–631 (2010)
Mountney, P., Stoyanov, D., Davison, A., Yang, G.-Z.: Simultaneous stereoscope localization and soft-tissue mapping for minimal invasive surgery. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 347–354. Springer, Heidelberg (2006). doi:10.1007/11866565_43
Mountney, P., Yang, G.-Z.: Motion compensated SLAM for image guided surgery. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 496–504. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15745-5_61
Collins, T., Bartoli, A., Bourdel, N., Canis, M.: Robust, real-time, dense and deformable 3D organ tracking in laparoscopic videos. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 404–412. Springer, Cham (2016). doi:10.1007/978-3-319-46720-7_47
Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.M.M.: ORBSLAM-based endoscope tracking and 3D reconstruction. arXiv preprint (2016). arXiv:1608.08149
Puerto-Souza, G.A., Mariottini, G.-L.: A fast and accurate feature-matching algorithm for minimally-invasive endoscopic images. IEEE Trans. Med. Imaging 32(7), 1201–1214 (2013)
Li, W., Song, D.: Featureless motion vector-based simultaneous localization, planar surface extraction, and moving obstacle tracking. In: Akin, H.L., Amato, N.M., Isler, V., van der Stappen, A.F. (eds.) Algorithmic Foundations of Robotics XI. STAR, vol. 107, pp. 245–261. Springer, Cham (2015). doi:10.1007/978-3-319-16595-0_15
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2007, pp. 225–234 (2007)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)
Mountney, P., Stoyanov, D., Yang, G.Z.: Three-dimensional tissue deformation recovery and tracking. IEEE Signal Process. Magaz. 27(4), 14–24 (2010)
Lee, G.H., Fraundorfer, F., Pollefeys, M.: RS-SLAM: RANSAC sampling for visual FastSLAM. In: International Conference on Intelligent Robots and Systems, pp. 1655–1660 (2011)
Acknowledgments
This work was supported by the MEXT, the JSPS KAKENHI Grant Numbers 25242047, 26108006, 26560255, 17H00869. We thanks to our lab colleagues for their help.
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Wang, C., Oda, M., Hayashi, Y., Misawa, K., Roth, H., Mori, K. (2017). Motion Vector for Outlier Elimination in Feature Matching and Its Application in SLAM Based Laparoscopic Tracking. In: Cardoso, M., et al. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE CLIP 2017 2017. Lecture Notes in Computer Science(), vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_6
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DOI: https://doi.org/10.1007/978-3-319-67543-5_6
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