Hierarchical Exploration of Volumes Using Multilevel Segmentation

1. Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms Cheuk Yiu Ip Amitabh Varshney Joseph JaJa

2. Volume Exploration Challenge Raw Volume Data Cube Meaningful Visualization [Voreen CG&A 09]

3. Transfer Function Evolution How do we find the “right” transfer function? RGBA Intensity

4. Histogram Helps Transfer Function Design [Drebin et al. SIGGRAPH 88]

5. Histogram Helps Transfer Function Design [Drebin et al. SIGGRAPH 88]

6. Histogram Helps Transfer Function Design [Drebinet al. SIGGRAPH 88]

7. Histogram Helps Transfer Function Design

8. Histograms May Not Always Help

9. Volume Exploration Exhaustively explore the dataset

10. Volume Exploration Seek for salient features

11. Volume Exploration Seek for salient features

12. Volume Exploration Seek for salient features

13. Volume Exploration Feature locations can be arbitrary

14. 2D Transfer Functions Gradient ( f’(x) ) captures boundaries Histogram shapes ⇒ Volume segments [Kindlmann & Durkin VolVis98, Kniss et al. TVCG 02] Gradient Intensity

15. Advances on New Attributes Higher Derivatives: f”(x)[Kindlmann & Durkin VolVis98] Specific features: Size [Correa and Ma TVCG 08] LH-transform [Seredaet al. TVCG 06], Domain specific semantic attributes [Salamaet al. TVCG 06] Select good views: Visibility [Correa and Ma TVCG 10], Information divergence [Ruiz et al. TVCG 11]

16. Challenges of the 2D Transfer Function Search for separated meaningful features • 1 Region 1 Minute

17. Histogram and Volume Features

18. Histogram and Volume Features Skin

19. Histogram and Volume Features Flesh

20. Histogram and Volume Features Skull

21. Histogram and Volume Features Sinus

22. Histogram and Volume Features Teeth

23. Approximate Histogram Transfer Functions Existing approaches directly or indirectly fit the histogram [Wang et al. TVCG 2011] User Specified [Knisset al. TVCG 02, Fogalet al. 2010]

24. Reduce Search to Classification Recursive histogram classification Tight coverage with a few segments Exhaustive exploration

25. Overview • Segment the histogram statistics • Build an exhaustive multilevel hierarchy • User interactive exploration

26. Overview Visual segmentation matches user intuition Complete coverage Users stay in a familiar feature space

27. Intensity-gradient Histogram Users implicitly recognize shapes from the histogram Segment this histogram as an image Gradient Intensity

28. Normalized-cut Image Segmentation Normalized-cut (ncut) image segmentation [Shi & Malik PAMI 98] Min ncut produces balanced segments Eigenanalysis approximates the min ncut for k segments B [Wang et al. PATTERN RECOGN LETT 06]

29. Normalized-cut on Intensity-gradient Histogram We apply normalized-cut on 2562 8-bit histograms k=2separates the tooth from the volume box k=10shows segments of the tooth crown and root k=20 shows different material boundaries

30. Which k should we pick ? Iteratively picking k is tedious Increasing k may not subdivide region of user interest Try k = 2, 3, 4, …

31. Replace k with User-driven Exploration Multilevel Segmentation Hierarchy: Apply normalized-cut recursively

32. Multilevel Segmentation Hierarchy Selectively inspect segments of choice

33. Multilevel Segmentation Hierarchy Any cut guarantees complete coverage View-dependent LoD hierarchies [Xia & Varshney Vis 96, Hoppe SIGGRAPH 97, Luebke &Erikson SIGGRAPH 97]

34. Multilevel Segmentation Hierarchy

35. Information Guided Traversal Segment entropy High entropy ⇒ Complex segment 6.8 Entropy 2.8

36. What if the Entropies are Similar… The segment entropies can be similar Which segment should we divide next? Use Information Gain Entropy

37. Information-Gain Guided Traversal Information Gain = Entropy reduction after a subdivision High Information Gain ⇒ Structural separation 0.01 Information Gain 0.11

38. Interactive Exploration Explore the segmentation hierarchy Selective expansion Interactive visualizations Exhaustively explore the tooth in 1 minute

39. Examples Engine block Visible Human Male Head

40. Examples Tomato Hurricane Isabel

41. Conclusions Computational segmentation mimics user interactions Intuitive volumetric classification Exhaustive multilevel hierarchy Information guided traversal Interactive exploration

42. Future Work Improve the information content measures Automatic color assignment for segments Segment histograms with different attributes Time varying datasets

43. Acknowledgements National Science Foundation: CCF 05-41120, CMMI 08-35572, CNS 09-59979 NVIDIA CUDA Center of Excellence Program Derek Juba, SujalBista, Yang Yang, M. AdilYalcin, and the reviewers for improving this paper and presentation The SciVis Best Paper Award committee Thank you!

44. Questions ? Source code for building the hierarchy: www.cs.umd.edu/~ipcy/software/volsegtree/ Papers and videos: Cheuk Yiu Ip www.cs.umd.edu/~ipcy/ GVIL Research Highlights www.cs.umd.edu/gvil/