1. Volume Visualization Acknowledgements: Torsten Möller (SFU)

2. 2 Direct Volume Rendering Streamlines Line Integral Convolution Glyphs Isosurfaces SciVis Scatter Plots Parallel Coordinates Node-link Diagrams InfoVis Verma et al. Hauser et al. Cabral & Leedom Fua et al. Lamping et al.

3. 3 Overview ► Data & Applications ► Slicer tools ► Iso-surfaces ► Direct Volume Rendering ► Challenges

4. 4 Medical Scanning ► MRI, CT, SPECT, PET, ultrasound

5. 5 Medical Scanning - Applications ► Diagnosis ► Surgery planning ► Tele-medicine ► Inter-operative visualization in brain surgery, biopsies, etc.

6. 6 Medical Scanning - Applications ► Medical education for anatomy, surgery, etc. ► Illustration of medical procedures to the patient

7. 7 Biological Scanning ► Scanners: Biological scanners, electronic microscopes, confocal microscopes ► Apps – physiology, paleontology, microscopic analysis…

8. 8 Industrial Scanning ► Planning (e.g., log scanning) ► Quality control ► Security (e.g. airport scanners)

9. 9 Scientific Computation - Domain ► Mathematical analysis ► ODE/PDE (ordinary and partial differential equations) ► Finite element analysis (FE) ► Supercomputer simulations

10. 10 Scientific Computation - Apps ► Flow Visualization (next week) Tecplot.com Compassis.com

11. 11 Data Dimensionality ► 3D (spatial data) or 4D (3D spatial + time) ► May be multivariate (several data values at each point) Voxel

12. 12 Overview ► Data & Applications ► Slicer tools ► Iso-surfaces ► Direct Volume Rendering ► Challenges

13. 13 3D Slicer Volume Slicer McGuffin et al.

14. 14 Overview ► Data & Applications ► Slicer tools ► Iso-surfaces ► Direct Volume Rendering ► Challenges

15. 15 Isosurfaces - Examples Isolines Isosurfaces Tecplot.com

16. 16 Isosurface Extraction ► by contouring  “marching cubes” is most common method 1234 3 2786 2 3797 3 1366 3 0113 2 Iso-value = 5

17. 17 MC: Classify Each Voxel ► Each voxel is either - outside the surface (> isovalue) - inside the surface ( isovalue) - inside the surface (<= isovalue) 8 Iso=7 8 8 5 5 10 Iso=9 =inside =outside

18. 18 MC: Assign Triangles ► all 256 cases can be derived from 15 base cases

19. 19 MC: Example a b c

20. 20 MC: Find Edge Locations ► For each triangle edge, find the vertex location along the edge using linear interpolation of the voxel values =10 =0 Isovalue = 8Isovalue = 3 i i+1 x

21. 21 MC: Compute Normals ► Calculate the normal at each cube vertex using central differencing: ► Use linear interpolation to compute the polygon vertex normal

22. 22 MC: Render!

23. 23 Overview ► Data & Applications ► Slicer tools ► Iso-surfaces ► Direct Volume Rendering ► Challenges

24. 24 Direct Volume Rendering Examples

25. 25 Rendering Pipeline Classify

26. 26 Classification ► original data set has application specific values (temperature, velocity, proton density, etc.) ► assign these to color/opacity values to make sense of data ► achieved through transfer functions

27. 27 Transfer Functions ► Simple (usual) case: Map data value to color and opacity Human Tooth CT opacity RGBRGB Data Value RGBRGB Shading, Compositing… Gordon Kindlmann opacity

28. 28 Transfer Functions ► Setting transfer functions is difficult and unintuitive University of Utah

29. 29 Transfer Function Challenges ► Better interfaces:  Make space of TFs less confusing  Remove excess “flexibility”  Provide guidance ► Automatic / semi-automatic transfer function generation  Typically highlight boundaries

30. 30 Rendering Pipeline Classify Shade

31. 31 Light Effects ► Usually only consider reflected part Light absorbed transmitted reflected Light=refl.+absorbed+trans. Light ambient specular diffuse Light=ambient+diffuse+specular

32. 32 Rendering Pipeline Classify Shade Interpolate

33. 33 Interpolation ► Given: ► Needed: 2D 1D ► Given: ► Needed:

34. 34 Interpolation ► Accuracy is important ► Expensive => done very often for one image Nearest neighbor Linear

35. 35 Rendering Pipeline Classify Shade Interpolate Composite

36. 36 Volumetric Ray Tracing color opacity object (color, opacity)

37. 37 Ray Traversal Schemes Depth Intensity Max Average Accumulate First

38. 38 Ray Traversal - First Depth Intensity First ► First: extracts iso-surfaces (again!) done by Tuy&Tuy ’84

39. 39 Ray Traversal - Average Depth Intensity Average ► Average: produces basically an X-ray picture

40. 40 Ray Traversal - MIP Depth Intensity Max ► Max: Maximum Intensity Projection used for Magnetic Resonance Angiogram

41. 41 Ray Traversal - Accumulate Depth Intensity Accumulate ► Accumulate: make transparent layers visible! V. Anupam et al.

42. 42 Volumetric Ray Integration color opacity object (color, opacity) 1.0 V. Anupam et al.

43. 43

44. Slicer Isosurface Direct Volume Rendering

45. 45 Overview ► Data & Applications ► Slicer tools ► Iso-surfaces ► Direct Volume Rendering ► Challenges

46. 46 Challenges - Accuracy ► Need metrics -> perceptual metric OriginalBias-addedEdge-distorted

47. 47 Challenges - Accuracy ► Deal with unreliable data (e.g., Ultrasound data is noisy)

48. 48 Challenges - Speed/Size ► Efficient algorithms ► Hardware developments (VolumePro) ► Utilize current hardware (nvidia, ATI) ► Compression schemes ► Tera-byte data sets

49. 49 Challenges - HCI ► Need better interfaces for specifying parameters ► Which method is best?

50. 50 Challenges - HCI ► Virtual and Augmented reality ► Explore novel I/O devices Konieczny et al., Vis 2005Schkolne et al.

51. 51 Challenges: Highlighting Important Information ► Illustrative Rendering ► Focus + Context Christian Tietjen Viola et al.