The Visual Computer

, Volume 27, Issue 5, pp 347–363 | Cite as

Image-based rendering of intersecting surfaces for dynamic comparative visualization

  • Stef BuskingEmail author
  • Charl P. Botha
  • Luca Ferrarini
  • Julien Milles
  • Frits H. Post
Original Article


Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503–510, 2005). In this paper we present an image-based formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading.

We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regarding the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface’s distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface.

Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models.


Comparative visualization Image-based rendering Surface comparison Nested surfaces 


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  1. 1.
    Weigle, C., Taylor, R.M. II: Visualizing intersecting surfaces with nested-surface techniques. In: IEEE Visualization, Proceedings, pp. 503–510 (2005). doi: 10.1109/VISUAL.2005.1532835 Google Scholar
  2. 2.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995). doi: 10.1006/cviu.1995.1004 CrossRefGoogle Scholar
  3. 3.
    Pagendarm, H.G., Post, F.H.: Comparative visualization—approaches and examples. In: Visualization in Scientific Computing, pp. 95–108. Springer, Berlin (1995) Google Scholar
  4. 4.
    Rey, D., Subsol, G., Delingette, H., Ayache, N.: Automatic detection and segmentation of evolving processes in 3D medical images: application to multiple sclerosis. Med. Image Anal. 6, 163–179 (2002). doi: 10.1016/S1361-8415(02)00056-7 CrossRefGoogle Scholar
  5. 5.
    Busking, S., Botha, C.P., Post, F.H.: Direct visualization of deformation in volumes. In: Hege, H.C., Hotz, I., Munzner, T. (eds.) Eurographics/IEEE-VGTC Symposium on Visualization, vol. 28, pp. 799–806 (2009). doi: 10.1111/j.1467-8659.2009.01471.x Google Scholar
  6. 6.
    Subsol, G., Roberts, N., Doran, M., Thirion, J.P., Whitehouse, G.H.: Automatic analysis of cerebral atrophy. Magn. Reson. Imaging 15, 917–927 (1997). doi: 10.1016/S0730-725X(97)00002-7 CrossRefGoogle Scholar
  7. 7.
    Wilson, D.L., Baddeley, A.J., Owens, R.A.: A new metric for grey-scale image comparison. Int. J. Comput. Vis. 24, 5–17 (1997). doi: 10.1023/A:1007978107063 CrossRefGoogle Scholar
  8. 8.
    di Gesú, V., Starovoitov, V.: Distance-based functions for image comparison. Pattern Recogn. Lett. 20, 207–214 (1999). doi: 10.1016/S0167-8655(98)00115-9 CrossRefzbMATHGoogle Scholar
  9. 9.
    Miranda, P.A.V., da Torres, S.R., Falcao, A.X.: TSD: a shape descriptor based on a distribution of tensor scale local orientation. In: SIBGRAPI, Proceedings, pp. 139–146 (2005). doi: 10.1109/SIBGRAPI.2005.51 Google Scholar
  10. 10.
    Veltkamp, R.C.: Shape matching: similarity measures and algorithms. In: IEEE Shape Modeling and Applications, Proceedings, pp. 188–197 (2001). doi: 10.1109/SMA.2001.923389 CrossRefGoogle Scholar
  11. 11.
    Li, X., He, Y., Gu, X., Qin, H.: Curves-on-surface: a general shape comparison framework. In: IEEE Shape Modeling and Applications, Proceedings, pp. 38–43 (2006). doi: 10.1109/SMI.2006.8 Google Scholar
  12. 12.
    Masuda, T., Imazu, S., Auethavekiat, S., Furuya, T., Kawakami, K., Ikeuchi, K.: Shape difference visualization for ancient bronze mirrors through 3D range images. J. Vis. Comput. Animat. 14, 183–196 (2003). doi: 10.1002/vis.316 CrossRefGoogle Scholar
  13. 13.
    Gatzke, T., Grimm, C., Garland, M., Zelinka, S.: Curvature maps for local shape comparison. In: IEEE Shape Modeling and Applications, Proceedings, pp. 244–253 (2005). doi: 10.1109/SMI.2005.13 Google Scholar
  14. 14.
    Lim, I.S., Sarni, S., Thalmann, D.: Colored visualization of shape differences between bones. In: IEEE Computer Based Medical Systems, Proceedings, pp. 26–27 (2003) Google Scholar
  15. 15.
    Pichon, E., Nain, D., Niethammer, M.: A Laplace equation approach for shape comparison. In: SPIE Medical Imaging, Proceedings, vol. 6141, pp. 373–382 (2006) Google Scholar
  16. 16.
    Tory, M., Möller, T., Atkins, M.S.: Visualization of time-varying MRI data for MS lesion analysis. In: SPIE Medical Imaging, Proceedings, vol. 4319, pp. 590–598 (2001) Google Scholar
  17. 17.
    Johnson, C.R., Sanderson, A.R.: A next step: visualizing errors and uncertainty. IEEE Comput. Graph. Appl. 23, 6–10 (2003). doi: 10.1109/MCG.2003.1231171 CrossRefGoogle Scholar
  18. 18.
    Rheingans, P.: Opacity-modulating triangular textures for irregular surfaces. In: IEEE Visualization, Proceedings, pp. 219–225 (1996) Google Scholar
  19. 19.
    Interrante, V., Fuchs, H., Pizer, S.: Conveying the 3D shape of smoothly curving transparent surfaces via texture. In: IEEE Transactions on Visualization and Computer Graphics, pp. 98–117 (1997) Google Scholar
  20. 20.
    Bair, A., House, D.: A grid with a view: optimal texturing for perception of layered surface shape. IEEE Trans. Vis. Comput. Graph. 13, 1656–1663 (2007). doi: 10.1109/TVCG.2007.70559 CrossRefGoogle Scholar
  21. 21.
    Bruckner, S., Grimm, S., Kanitsar, A., Gröller, M.E.: Illustrative context-preserving volume rendering. In: Eurographics/IEEE-VGTC Symposium on Visualization, vol. 1, pp. 69–76 (2005) Google Scholar
  22. 22.
    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). doi: 10.1109/TVCG.2006.96. CrossRefGoogle Scholar
  23. 23.
    Weigle, C.: Displays for exploration and comparison of nested or intersecting surfaces. Ph.D. thesis (2006) Google Scholar
  24. 24.
    Williams, L.: Casting curved shadows on curved surfaces. In: Computer Graphics and Interactive Techniques, pp. 270–274 (1978). doi: 10.1145/800248.807402 Google Scholar
  25. 25.
    Goldfeather, J., Molnar, S., Turk, G., Fuchs, H.: Near real-time CSG rendering using tree normalization and geometric pruning. IEEE Comput. Graph. Appl. 9, 20–28 (1989). doi: 10.1109/38.28107 CrossRefGoogle Scholar
  26. 26.
    Wiegand, T.F.: Interactive rendering of CSG models. Comput. Graph. Forum 15, 249–261 (1996) CrossRefGoogle Scholar
  27. 27.
    Mammen, A.: Transparency and antialiasing algorithms implemented with the virtual pixel maps technique. IEEE Comput. Graph. Appl. 9, 43–55 (1989). doi: 10.1109/38.31463 CrossRefGoogle Scholar
  28. 28.
    Diefenbach, P.: Pipeline rendering: interaction and realism through hardware-based multi-pass rendering. Ph.D. thesis (1996) Google Scholar
  29. 29.
    Everitt, C.: Interactive order-independent transparency. Tech. rep., NVIDIA (2001). URL
  30. 30.
    Deering, M., Winner, S., Schediwy, B., Duffy, C., Hunt, N.: The triangle processor and normal vector shader: a VLSI system for high performance graphics. In: ACM SIGGRAPH, Proceedings, vol. 22, pp. 21–30 (1988) CrossRefGoogle Scholar
  31. 31.
    Saito, T., Takahashi, T.: Comprehensible rendering of 3-D shapes. In: ACM SIGGRAPH, Proceedings, pp. 197–206 (1990). CrossRefGoogle Scholar
  32. 32.
    Guennebaud, G., Barthe, L., Paulin, M.: Splat/mesh blending, perspective rasterization and transparency for point-based rendering. In: IEEE/Eurographics/ACM Symposium on Point-Based Graphics, pp. 49–58 (2006) Google Scholar
  33. 33.
    Nienhaus, M., Kirsch, F., Döllner, J.: Illustrating design and spatial assembly of interactive CSG. In: Computer Graphics, Virtual Reality, Visualization and Interaction in {Africa}, Proceedings, pp. 91–98 (2006). doi: 10.1145/1108590.1108605 Google Scholar
  34. 34.
    Mauch, S.: A fast algorithm for computing the closest point and distance transform. Tech. rep., CalTech (2000) Google Scholar
  35. 35.
    Peikert, R., Sigg, C.: Optimized Bounding Polyhedra for GPU-Based Distance Transform. Springer, Berlin Heidelberg (2006), pp. 65–77. doi: 10.1007/3-540-30790-7_5 Google Scholar
  36. 36.
    Bavoil, L., Callahan, S.P., Lefohn, A., Comba, J.L.D., Silva, C.T.: Multi-fragment effects on the GPU using the K-buffer. In: ACM i3D, Proceedings, pp. 97–104 (2007). Google Scholar
  37. 37.
    Ferrarini, L., Palm, W.M., Olofsen, H., van Buchem, M.A., Reiber, J.H.C., Admiraal-Behloul, F.: Shape differences of the brain ventricles in Alzheimer’s disease. Neuroimage 32, 1060–1069 (2006). doi: 10.1016/j.neuroimage.2006.05.048 CrossRefGoogle Scholar
  38. 38.
    Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. 22(140), 1–55 (1932) Google Scholar
  39. 39.
    Busking, S., Botha, C.P., Post, F.H.: Dynamic multi-view exploration of shape spaces. In: Melançon, G., Munzner, T., Weiskopf, D. (eds.) Eurographics/IEEE-VGTC Symposium on Visualization, vol. 29, pp. 973–982 (2010). doi: 10.1111/j.1467-8659.2009.01684.x Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Stef Busking
    • 1
    Email author
  • Charl P. Botha
    • 1
    • 2
  • Luca Ferrarini
    • 2
  • Julien Milles
    • 2
  • Frits H. Post
    • 1
  1. 1.Data Visualization GroupDelft University of TechnologyDelftthe Netherlands
  2. 2.Division of Image Processing (LKEB), Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands

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