Journal of Digital Imaging

, Volume 27, Issue 2, pp 207–219 | Cite as

Generalized Temporal Focus + Context Framework for Improved Medical Data Exploration

Article

Abstract

Physicians use slices and 3D volume visualizations to place a diagnosis, establish a treatment plan and as a guide during surgical procedures. There is an observed difference in 2D and 3D visualization objectives of the various groups of specialists. We describe a generalized temporal focus + context framework that unifies different widely used and novel visualization methods. The framework is used to classify already existing common techniques and to define new techniques that can be used in medical volume visualization. The new techniques explore the time-dependent position of the framework focus region to combine 2D and 3D rendering inside the focus and to provide a new focus-driven context region that gives explicit spatial perception cues between the current and past regions of interest. An arbitrary-shaped focus region and no context rendering are two novel framework-based techniques that support improved planning of procedures that involve drilling or endoscopic exploration. The new techniques are quantitatively compared to already existing techniques by means of a user study.

Keywords

Volume visualization Visual perception Evaluation studies Focus + context Generalized framework 

References

  1. 1.
    Kainz B, Portugaller RH, Seider D, Moche M, Stiegler P, Schmalstieg D: Volume visualization in the clinical practice. In: Proceedings of the 6th international conference on Augmented Environments for Computer-Assisted Interventions. Berlin, Springer-Verlag, 2011, 74-84Google Scholar
  2. 2.
    Viega J, Conway MJ, Williams G, Pausch R: 3D magic lenses. In: Proceedings of the 9th annual ACM symposium on User interface software and technology. 1996, 51-58Google Scholar
  3. 3.
    MeVisLab. Medical Image Processing and Visualization. Available at: www.mevislab.de/. Accessed on Oct 09, 2013
  4. 4.
    Slicer3D. Multiplatform, free and open source software package for visualization and medical image computing. Available at: www.slicer.org/. Accessed on Oct 09, 2013
  5. 5.
    Caban JJ, Joshi A, Nagy P: Rapid development of medical imaging tools with open-source libraries. J Digit Imaging 20(supplement 1):83–93, 2007PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    GEHC MicroView, Parallax Innovations. Available at http://microview.sourceforge.net/. Accessed 5 November 2012
  7. 7.
    Hachaj T, Ogiela MR: Framework for cognitive analysis of dynamic perfusion computed tomography with visualization of large volumetric data. J Electron Imaging, 21(4), Article Number: 043017, 2012Google Scholar
  8. 8.
    Tietjen C, Meyer B, Schlechtweg S, Preim B, Hertel I, Strauß G: Enhancing slice-based visualizations of medical volume data. In: Proceedings of EuroVis’06, 2006, 123-130Google Scholar
  9. 9.
    Tory M, Swindells C: Exovis: An overview and detail technique for volumes. Technical Report SFU-CMPTTR2002-05, Computing Science Dept., Simon Fraser University, 2002Google Scholar
  10. 10.
    König A, Doleisch H, Gröller ME: Multiple views and magic mirrors—fMRI visualization of the human brain. TR-186-2-99-08, 1999Google Scholar
  11. 11.
    Bruckner S, Gröller ME: Exploded views for volume data. IEEE Trans Vis Comput Graphics 12(5):1077–1084, 2006CrossRefGoogle Scholar
  12. 12.
    Weiskopf D, Engel K, Ertl T: Interactive clipping techniques for texture-based volume visualization and volume shading. IEEE Trans Vis Comput Graphics 9(3):298–312, 2003CrossRefGoogle Scholar
  13. 13.
    Correa C, Silver D, Chen M: Feature aligned volume manipulation for illustration and visualization. IEEE Trans Vis Comput Graphics 12(5):1069–1076, 2006CrossRefGoogle Scholar
  14. 14.
    Sarkar M, Brown MH: Graphical fisheye views of graphs. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, 1992, 83-91Google Scholar
  15. 15.
    Pindat C, Pietriga E, Chapuis O, Puech C: JellyLens: content-aware adaptive lenses. In: Proceedings of the 25th annual ACM symposium on User interface software and technology, 2012, 261-270Google Scholar
  16. 16.
    Borst CW, Tiesel JP, Best CM: Real-time rendering method and performance evaluation of composable 3D lenses for interactive VR. IEEE Trans Vis Comput Graphics 16(3):394–410, 2010CrossRefGoogle Scholar
  17. 17.
    LaMar E, Hamann B, Joy KI: A magnification lens for interactive volume visualization. In: Proc. 9th Pacific Conf. Computer Graphics and Applications, IEEE Press, 2001, 223-233Google Scholar
  18. 18.
    Yang Y, Chen JX, Beheshti M: Nonlinear perspective projections and magic lenses: 3D view deformation. IEEE Comput Graph Appl 25(1):76–84, 2005PubMedCrossRefGoogle Scholar
  19. 19.
    Wang L, Zhao Y, Mueller K, Kaufman A: The magic volume lens: an interactive focus + context technique for volume rendering. IEEE Visualization, 2005, 367-374Google Scholar
  20. 20.
    Rossler F, Botchen R. P., Ertl T: Dynamic shader generation for GPU-based multi-volume ray casting. IEEE Comput. Graph. Appl., 28(5):66-77, 2008Google Scholar
  21. 21.
    Plate J, Holtkaemper T, Froehlich B: A flexible multi-volume shader framework for arbitrarily intersecting multi-resolution datasets. IEEE Trans Vis Comput Graphics 13(6):1584–1591, 2007CrossRefGoogle Scholar
  22. 22.
    Kirmizibayrak C, Yim Y, Wakid M, Hahn J: Interactive visualization and analysis of multimodal datasets for surgical applications. J Digit Imaging 25(6):792–801, 2012PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Svakhine N, Ebert DS, Stredney D: Illustration motifs for effective medical volume illustration. IEEE Comput Graph Appl 25(3):31–39, 2005PubMedCrossRefGoogle Scholar
  24. 24.
    Diepenbrock S, Praßni JS, Lindemann F, Bothe HW, Ropinski T: Interactive visualization techniques for neurosurgery planning. In: Proceedings of Eurographics, 2011, 13-16Google Scholar
  25. 25.
    Rieder C, Ritter F, Raspe M, Peitgen HO: Interactive visualization of multimodal volume data for neurosurgical tumor treatment. Computer Graphics Forum 27(3):1055–1062, 2008CrossRefGoogle Scholar
  26. 26.
    Burns M, Haidacher M, Wein W, Viola I, Gröller ME: Feature emphasis and contextual cutaways for multimodal medical visualization. In: Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization. Eurographics Association, 2007, 275-282Google Scholar
  27. 27.
    Gasteiger R, Neugebauer M, Beuing O, Preim B: The FLOWLENS: a focus-and-context visualization approach for exploration of blood flow in cerebral aneurysms. IEEE Trans Vis Comput Graphics 17(12):2183–2192, 2011CrossRefGoogle Scholar
  28. 28.
    Viola I, Kanitsar A, Groller M. E.: Importance-driven volume rendering. In: Proceedings of the conference on Visualization ‘04 (VIS ‘04). IEEE Computer Society, Washington, DC, USA, 2004, 139-146Google Scholar
  29. 29.
    Hauser H, Mroz L, Italo Bischi G, Groller ME: Two-level volume rendering. IEEE Trans Vis Comput Graphics 7(3):242–252, 2001CrossRefGoogle Scholar
  30. 30.
    Luo Y: Distance-based focus + context models for exploring large volumetric medical datasets. Comput Sci Eng 14(5):63–71, 2012CrossRefGoogle Scholar
  31. 31.
    Sikachev P, Rautek P, Bruckner S, Gröller ME: Dynamic focus + context for volume rendering. In: Proceedings of Vision, Modeling and Visualization, 2010, 331-338Google Scholar
  32. 32.
    Kirmizibayrak C: “Interactive volume visualization and editing methods for surgical applications”. Ph.D. Dissertation, Department of Computer Science, The George Washington University, Washington, DC, 2011Google Scholar
  33. 33.
    Rogalla P, Terwisscha Van Scheltinga J, Jamm B: Virtual endoscopy and related 3d techniques. Springer-Verlag New York, LLC, 2001Google Scholar
  34. 34.
    Olwal A, Frykholm O, Groth K, Moll J: Design and evaluation of interaction technology for medical team meetings. In: Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction—Volume Part I, Springer, Berlin, Heidelberg, 2011, 505–522Google Scholar
  35. 35.
    Li J, Robertson T, Hansen S, Mansfield T, Kjeldskov J: Multidisciplinary medical team meetings: a field study of collaboration in health care. In: Proceedings of the 20th Australasian Conference on Computer-Human Interaction: Designing for Habitus and Habitat, ACM, New York, NY, USA, 2008, 73-80Google Scholar
  36. 36.
    Groth K, Frykholm O, Segersvard R, Isaksson B, Permert J: Efficiency in treatment discussions: a field study of time related aspects in multi-disciplinary team meetings. 22nd IEEE International Symposium on Computer-Based Medical Systems, 2009, 1–8Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceThe George Washington UniversityWashingtonUSA
  2. 2.Department of RadiologyThe George Washington UniversityWashingtonUSA

Personalised recommendations