Abstract
We propose a two-handed direct manipulation system to achieve complex volume segmentation of CT/MRI data in Augmented Reality with a remote controller attached to a motion tracking cube. At the same time segmented data is displayed by direct volume rendering using a programmable GPU. Our system achieves visualization of real time modification of volume data with complex shading including transparency control by changing transfer functions, displaying any cross section, and rendering multi materials using a local illumination model. Our goal is to build a system that facilitates direct manipulation of volumetric CT/MRI data for segmentation in Augmented Reality. Volume segmentation is a challenging problem and segmented data has important roles for visualization and analysis.
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Artoolkit http://www.hitl.washington.edu/artoolkit/.
M. Fiala. Artag, a fiducial marker system using digital techniques. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), pages 590–596, 2005.
J. Goble, K. Hinckley, R. Pausch, J. W. Snell, and N. Kassell. Two-handed spatial interface tools for neurosurgical planning. IEEE Computer, 28(7):20–26, 1995.
M. Ikits, J. Kniss, A. Lefohn, and C. Hansen. Volume rendering techniques. In GPU Gems, pages 667–692. 2004.
H. Kato and M. Billinghurst. Marker tracking and hmd calibration for a video-based augmented reality conferencing system. In Proceedings of the 2nd International Workshop on Augmented Reality (IWAR 99), pages 85–94, 1999.
H. Kato, M. Billinghurst, I. Poupyrev, K. Imamoto, and K. Tachibana. Virtual object manipulation on a table-top ar environment. In Proceedings of the International Symposium on Augmented Reality (ISAR 2000), pages 111–119, 2000.
S. Osher and R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer Verlag New York, 2003.
S. Owada, F. Nielsen, and T. Igarashi. Volume catcher. In ACM Symposium on Interactive 3D Graphics and Games 2005, pages 111 – 116, 2005.
D. L. Pham, C. Xu, and J. L. Prince. A survey of current methods in medical image segmentation. In Technical Report JHU/ECE 99-01. The Johns Hopkins University, 1999.
J. A. Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press, 1999.
A. Sharf, T. Lewiner, A. Shamir, L. Kobbelt, and D. CohenOr. Competing fronts for coarse-to-fine surface reconstruction. In Eurographics 2006, 2006.
A. Sherbondy, M. Houston, and S. Napel. Fast volume segmentation with simultaneous visualization using programmable graphics hardware. In VIS ’03: Proceedings of the 14th IEEE Visualization 2003 (VIS’03), page 23, Washington, DC, USA, 2003. IEEE Computer Society.
E. Vidholm, S. Nilsson, and I. Nyström. Fast and robust semi-automatic liver segmentation with haptic interaction. In MICCAI, pages 774–781, 2006.
Acknowledgements
This work was supported by KAKENHI (a Grant-in-Aid for Young Scientists (B)) (No. 21700126).
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Tawara, T. (2011). Interactive Volume Segmentation and Visualization in Augmented Reality. In: Furht, B. (eds) Handbook of Augmented Reality. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0064-6_8
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DOI: https://doi.org/10.1007/978-1-4614-0064-6_8
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