1. Authors: I. Viola, A. Kanitsar, M. Gr ö ler Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria Importance Driven Volume Rendering

2. Introduction (problem)  Volume visualization has inherent difficulties with occlusion Exterior objects occlude interior objects Interior objects might be more interesting or important

3. Introduction (motivation)  3D medical image visualization in general  Specific example: liver tumor visualization For radiotherapy planning For surgery planning  Need to clearly see: Tumor location, size, shape Blood vessel tree Parenchyma http://www.vislab.uq.edu.au/research/liver/images/3D_liver_model.jpg

4. Introduction (solution)  object importance  level of sparseness  make interesting objects occluded clearly visible Artistic medical illustration Method proposed in this paper

5. Related Work  Transfer Function Incorporate first and second derivatives (gradient and curvature values) Degree of interest (Hauser & Mlejnek)  Focus +Context Rendering Focus: Render that part with more detail, with magnification, or other enhancement Context: according to Distance to focal point, d etail or magnification gradually fades as distance increases

6. Related Work  Sparse Representation Interior structures — fully opaque Enclosing objects — curvature-directed lines  Cut-Away Views

7. Importance Driven Rendering  Importance is widely used in other graphics fields  Employ as additional dimension to improve the behavior of traditional approaches  Apply this in volume rendering

8. Importance Driven Volume Rendering  Object Importance  Importance Compositing  Levels of sparseness

9. Object Importance  Additional property besides color and opacity  Positive scalar value to show priority of each object importance  Constant for the whole object

10. Levels of sparseness Various different representations of a certain object from dense to sparse

11. Importance Compositing Specify the level of sparseness Simple way:Maximum importance projection (MImP) Average Importance Compositing

12. Importance Compositing  Mode 1: Maximum Importance Projection highest importance—visible Highest sparseness—transparent Assign sparseness level of 0 or 1 to each point 0 1 1

13. Problems with MImP  The spatial management of structures is not readily apparent.

14. Improved MImP  Consider MImP as a cut-away view Cylindrical Conical Countersink

16.  Mode 2: Average Importance Compositing Compute sum of object importances along a ray  Each surface point intersected by the ray is assigned a sparseness level based on the ratio of its importance to the sum of all intersected surface importances Importance Compositing 0 1 2

17. Smooth Transition with level of sparseness With Avg. Importance Compositing, generate jaggy object boundaries  Color and Opacity Modulation  Screen-Door Transparency  Volume Thinning

18.  Use transfer function to modulation the saturation of color and opacity Color and Opacity Modulation 0.25 0.50 0.75

19. Screen-Door Transparency  Semi-transparency  Object is Visible through the holes  Mesh occludes the object behind 0.25 0.50 0.75

20. Volume Thinning  Render objects using a set of iso- surfaces 0.25 0.50 0.75

21. Results  Pre-segmented objects Leopard Gecko 512*512*87 Monster Study 256*256*610

22. Conclusion  A new factor to traditional volume rendering—object importance  Apply level of sparseness if interesting structures are occlude  Methods to smooth transitions

23. Future work  How to automatically assign importance  Figure out the application in Volume Graphics  Incorporate 1 st and 2 nd order derivatives to preserve object boundaries in composition schemes  How to automatically determine optimal viewpoints