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Generalized Temporal Focus + Context Framework for Improved Medical Data Exploration


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.

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  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, 74-84

  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, 51-58

  3. MeVisLab. Medical Image Processing and Visualization. Available at: Accessed on Oct 09, 2013

  4. Slicer3D. Multiplatform, free and open source software package for visualization and medical image computing. Available at: Accessed on Oct 09, 2013

  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, 2007

    Article  PubMed Central  PubMed  Google Scholar 

  6. GEHC MicroView, Parallax Innovations. Available at Accessed 5 November 2012

  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, 2012

    Google Scholar 

  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, 123-130

  9. Tory M, Swindells C: Exovis: An overview and detail technique for volumes. Technical Report SFU-CMPTTR2002-05, Computing Science Dept., Simon Fraser University, 2002

  10. König A, Doleisch H, Gröller ME: Multiple views and magic mirrors—fMRI visualization of the human brain. TR-186-2-99-08, 1999

  11. Bruckner S, Gröller ME: Exploded views for volume data. IEEE Trans Vis Comput Graphics 12(5):1077–1084, 2006

    Article  Google Scholar 

  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, 2003

    Article  Google Scholar 

  13. Correa C, Silver D, Chen M: Feature aligned volume manipulation for illustration and visualization. IEEE Trans Vis Comput Graphics 12(5):1069–1076, 2006

    Article  Google Scholar 

  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, 83-91

  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, 261-270

  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, 2010

    Article  Google Scholar 

  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, 223-233

  18. Yang Y, Chen JX, Beheshti M: Nonlinear perspective projections and magic lenses: 3D view deformation. IEEE Comput Graph Appl 25(1):76–84, 2005

    Article  CAS  PubMed  Google Scholar 

  19. Wang L, Zhao Y, Mueller K, Kaufman A: The magic volume lens: an interactive focus + context technique for volume rendering. IEEE Visualization, 2005, 367-374

  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, 2008

    Google Scholar 

  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, 2007

    Article  Google Scholar 

  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, 2012

    Article  PubMed Central  PubMed  Google Scholar 

  23. Svakhine N, Ebert DS, Stredney D: Illustration motifs for effective medical volume illustration. IEEE Comput Graph Appl 25(3):31–39, 2005

    Article  PubMed  Google Scholar 

  24. Diepenbrock S, Praßni JS, Lindemann F, Bothe HW, Ropinski T: Interactive visualization techniques for neurosurgery planning. In: Proceedings of Eurographics, 2011, 13-16

  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, 2008

    Article  Google Scholar 

  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, 275-282

  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, 2011

    Article  Google Scholar 

  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, 139-146

  29. Hauser H, Mroz L, Italo Bischi G, Groller ME: Two-level volume rendering. IEEE Trans Vis Comput Graphics 7(3):242–252, 2001

    Article  Google Scholar 

  30. Luo Y: Distance-based focus + context models for exploring large volumetric medical datasets. Comput Sci Eng 14(5):63–71, 2012

    Article  Google Scholar 

  31. Sikachev P, Rautek P, Bruckner S, Gröller ME: Dynamic focus + context for volume rendering. In: Proceedings of Vision, Modeling and Visualization, 2010, 331-338

  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, 2011

  33. Rogalla P, Terwisscha Van Scheltinga J, Jamm B: Virtual endoscopy and related 3d techniques. Springer-Verlag New York, LLC, 2001

  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, 505–522

  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, 73-80

  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, 1–8

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The authors would like to thank all volunteers who participated in the user study, Prof. John Philbeck and Samar Alsaleh for their help in designing and executing the user study. This work has been sponsored by GW Centers and Institutes Facilitating Fund, The George Washington University.

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Correspondence to Nadezhda Radeva.

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Radeva, N., Levy, L. & Hahn, J. Generalized Temporal Focus + Context Framework for Improved Medical Data Exploration. J Digit Imaging 27, 207–219 (2014).

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  • Volume visualization
  • Visual perception
  • Evaluation studies
  • Focus + context
  • Generalized framework