Advertisement

Fast Contextual View Generation in 3D Medical Images Using a 3D Widget User Interface and Super-Ellipsoids

  • Ken Lagos
  • Tim McInerneyEmail author
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11845)

Abstract

This paper presents a 3D widget user interface (UI), super-ellipsoid shape primitives and a customized volume rendering algorithm that together create an effective system for generating contextual views in 3D medical images. The widget UI supports the fast and precise positioning of a super-ellipsoid “paint blob”. The paint blob can be deposited and automatically blended with previously deposited blobs to form an arbitrarily complex-shaped region of interest (ROI) enclosing target image features. The rendering of these “focus” regions can be controlled separately from the surrounding contextual region, allowing medical experts to examine and measure image features relative to the surrounding structures, regardless of the level of occlusion. The system’s core algorithms execute in parallel on graphics processing units, resulting in real-time interaction and high-quality visualizations. The focus plus context visualization system is validated via a user study and a series of experiments.

Keywords

Visualization 3D medical images User interface 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Ljung, P., Kruger, J.H., Groller, E., Hadwiger, M., Hansen, C.D., Ynnerman, A.: State of the art in transfer functions for direct volume rendering. Comput. Graph. Forum 35, 669–691 (2016)CrossRefGoogle Scholar
  2. 2.
    Faynshteyn, L., McInerney, T.: Context-preserving volumetric data set exploration using a 3D painting metaphor. In: Bebis, G., et al. (eds.) ISVC 2012. LNCS, vol. 7431, pp. 336–347. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33179-4_33CrossRefGoogle Scholar
  3. 3.
    Kriger, J., Schneider, J., Westermann, R.: ClearView: an interactive context preserving hotspot visualization technique. IEEE Trans. Vis. Comput. Graph. 12(5), 941–948 (2006)CrossRefGoogle Scholar
  4. 4.
    Bruckner, S., Grimm, S., Kanitsar, A., Gröller, M.E.: Illustrative context-preserving exploration of volume data. IEEE Trans. Vis. Comput. Graph. 12(6), 1559–1569 (2006)CrossRefGoogle Scholar
  5. 5.
    Tappenbeck, A., Preim, B., Dicken, V.: Distance-based transfer function design: specification methods and applications. In: Simulation and Visualization (2006)Google Scholar
  6. 6.
    Zhou, J., Döring, A., Tönnies, K.: Distance based enhancement for focal region based volume rendering. In: Tolxdorff, T., Braun, J., Handels, H., Horsch, A., Meinzer, H.P. (eds.) Bildverarbeitung für die Medizin 2004. Springer, Berlin (2004).  https://doi.org/10.1007/978-3-642-18536-6_41CrossRefGoogle Scholar
  7. 7.
    Monclus, E., Dıaz, J., Navazo, I., Vazquez, P.P.: The virtual magic lantern: an interaction metaphor for enhanced medical data inspection. In: The 16th ACM Symposium on Virtual Reality Software and Technology, Kyoto, Japan (2009)Google Scholar
  8. 8.
    Luo, Y., Iglesias Guitián, J.A., Gobbetti, E., Marton, F.: Context preserving focal probes for exploration of volumetric medical datasets. In: Magnenat-Thalmann, N. (ed.) 3DPH 2009. LNCS, vol. 5903, pp. 187–198. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-10470-1_16CrossRefGoogle Scholar
  9. 9.
    Ropinski, T., Steinicke, F., Hinrichs, K.: Tentative results in focus-based medical volume visualization. In: Butz, A., Fisher, B., Krüger, A., Olivier, P. (eds.) SG 2005. LNCS, vol. 3638, pp. 218–221. Springer, Heidelberg (2005).  https://doi.org/10.1007/11536482_19CrossRefGoogle Scholar
  10. 10.
    Bruckner, S., Gröller, M.: Volumeshop: an interactive system for direct volume illustration. In: 16th IEEE Conference on Visualization (VIS 2005), Baltimore, MD (2005)Google Scholar
  11. 11.
    Radeva, N., Levy, L., Hahn, J.: Generalized temoral focus + context framework for improved medical data exploration. J. Digit. Imaging 27, 207–219 (2014)CrossRefGoogle Scholar
  12. 12.
    Chen, H., Samavati, F., Sousa, M.: GPU-based point radiation for interactive volume sculpting and segmentation. Vis. Comput. 24(7), 689–698 (2008)CrossRefGoogle Scholar
  13. 13.
    LiveVolume. www.livevolume.com. Accessed 01 June 2017
  14. 14.
    Lagos, K.: Fast contextual view generation and region of interest selection in 3D medical images via superellipsoid manipulation, blending and constrained region growing. Master’s thesis, Department of Computer Science, Ryerson University, Toronto, ON, Canada (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceRyerson UniversityTorontoCanada

Personalised recommendations

Cite paper

Buy options