Data-Aware Picking for Medical Models

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 458)

Abstract

Medical doctors are often faced with the problem of selecting anchoring points in 3D space. These points are commonly used for measurement tasks, such as lengths of bones, or dimensions of pathological structures (e.g. tumours). Since previous research indicates that measurement tasks can be usually carried out more efficiently in VR environments than in desktop-based systems, we have concentrated on the development of selection tools for medical models. These models have a set of particularities such as the presence of semi-transparencies, and there is a lack of tools for measurement support for such models in VR environments. Our VR-based interaction technique uses the data to automatically generate candidate anchor points, and it is specially focused on the efficient selection of 3D points in datasets rendered using methods with semi-transparency such as Direct Volume Rendering. We will show that our method is effective, precise, and reduces the time and amount of movements required to set the anchor points as compared with other classical techniques based on clipping planes. We also provide a couple of improvements tailored to reduce the inherent imprecision of 3D devices due to hand vibration, and the flexibility in transfer function selection for anchor point definition.

Keywords

3D interaction 3D selection Medical visualization Virtual reality 

References

  1. 1.
    Reitinger, B., Schmalstieg, D., Bornik, A., Beichel, R.: Spatial analysis tools for virtual reality-based surgical planning. In: 3D User. Interfaces, pp. 37–44 (2006)Google Scholar
  2. 2.
    Monclús, E., Vázquez, P.P., Navazo, I.: DAAPMed: a data-aware anchor point selection tool for medical models in VR environments. In: VISIGRAPP - International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 308–317 (2013)Google Scholar
  3. 3.
    Monclús, E., Díaz, J., Navazo, I., Vázquez, P.P.: The virtual magic lantern: an interaction metaphor for enhanced medical data inspection. In: VRST ’09: Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology, pp. 119–122. ACM, New York (2009)Google Scholar
  4. 4.
    Hinckley, K., Pausch, R., Goble, J.: A three-dimensional user interface for neurosurgical visualization. In: The SPIE Conference on Medical Imaging, pp. 126–136. SPIE (1994)Google Scholar
  5. 5.
    Preim, B., Tietjen, C., Spindler, W., Peitgen, H.O.: Integration of measurement tools in medical 3D visualizations. In: Visualization ’02, pp. 21–28. IEEE Computer Society (2002)Google Scholar
  6. 6.
    Rössling, I., Cyrus, C., Dornheim, L., Boehm, A., Preim, B.: Fast and flexible distance measures for treatment planning. Int. J. Comput. Assist. Radiol. Surg. 5, 633–646 (2010)CrossRefGoogle Scholar
  7. 7.
    Hagedorn, J., Joy, P., Dunkers, S., Peskin, A.: Measurement tools for the immersive visualization environment: steps toward the virtual laboratory. J. Res. Nat. Inst. Stan. Technol. 112, 257–270 (2007)CrossRefGoogle Scholar
  8. 8.
    Hastreiter, P., Rezk-Salama, C., Tomandl, B., Eberhardt, K.E.W., Ertl, T.: Fast analysis of intracranial aneurysms based on interactive direct volume rendering and CTA. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 660–669. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Gallo, L., De Pietro, G., Marra, I.: 3D interaction with volumetric medical data: experiencing the wiimote. In: Proceedings of the 1st International Conference on Ambient Media and Systems, pp. 14:1–14:6 (2008)Google Scholar
  10. 10.
    Bowman, D., Kruijff, E., LaViola, J., Poupyrev, I.: 3D User Interfaces: Theory and Practice. Addison-Wesley, Pearson Education (2004)Google Scholar
  11. 11.
    Grossman, T., Balakrishnan, R.: The design and evaluation of selection techniques for 3D volumetric displays. In: Proceedings of the Symposium on User Interface Software and Technology, pp. 3–12. ACM (2006)Google Scholar
  12. 12.
    Hinckley, K., Pausch, R., Goble, J., Kassell, N.: A survey of design issues in spatial input. In: Proceedings of the Symposium on User Interface Software and Technology, pp. 213–222. ACM (1994)Google Scholar
  13. 13.
    Bowman, D.A., Johnson, D.B., Hodges, L.F.: Testbed evaluation of virtual environment interaction techniques. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 26–33 (1999)Google Scholar
  14. 14.
    König, A.H., Doleisch, H., Gröller, E., Brain, T.H.: Multiple views and magic mirrors - fmri visualization of the human brain (1999)Google Scholar
  15. 15.
    Monga, O., Deriche, R., Malandain, G., Cocquerez, J.P.: Recursive filtering and edge closing: two primary tools for 3-d edge detection. In: Faugeras, O. (ed.) ECCV 1990. LNCS, vol. 427, pp. 56–65. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  16. 16.
    Bowman, D.A., Wingrave, C.A., Campbell, J.M., Ly, V.Q., Rhoton, C.J.: Novel uses of pinch gloves for virtual environment interaction techniques. Virtual Reality 6, 122–129 (2002)CrossRefGoogle Scholar
  17. 17.
    Ware, C., Slipp, L.: Using velocity control to navigate 3D graphical environments: a comparison of three interfaces. In: Human Factors and Ergonomic Studies (HFES) Meeting, pp. 25–32 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Eva Monclús
    • 1
  • Pere-Pau Vázquez
    • 1
  • Isabel Navazo
    • 1
  1. 1.ViRVIG-LSIUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations