1. institute for vision and graphics university of siegen, germany High-Level User Interfaces for Transfer Function Design with Semantics Christof Rezk Salama (Univ. Siegen, Germany) Maik Keller (Univ. Siegen, Germany) Peter Kohlmann (TU Vienna, Austria)

2. christof rezk-salama, institute for vision and graphics, university of siegen Volume Visualization Volume visualization techniques are mature from the technical point of view. Real-time volume graphics on commodity PC hardware Multidimensional transfer functions/classification Gradient estimation and local illumination on-the-fly Memory management and compression for large volumes Even global illumination techniques. Is the volume rendering problem solved? If you ask the computer scientist, hell probably say yes. If you ask the users, they will most likely say no

3. christof rezk-salama, institute for vision and graphics, university of siegen Questions Why are volume rendering applications so hard to use for non- experts? Are volume rendering applications easy to use for us, the experts ? What features must appropriate user interfaces provide?

4. christof rezk-salama, institute for vision and graphics, university of siegen The Mental Model Example taken from: Donald A. Norman The Psychology of Everyday Things

5. christof rezk-salama, institute for vision and graphics, university of siegen Volume Visualization Transfer Function Design: Mapping of scalar data to optical properties (emission/absorption) Color table: Example: 1D TF for 12 bit Data, 4096 values x RGBA = 16384 DOF Editors based on geometric primitives 1D Transfer Functions 2D Transfer Functions

6. christof rezk-salama, institute for vision and graphics, university of siegen User Intention Examples: Fade out the soft tissue Sharpen the blood vessels Enhance the contrast Question: What actions are necessary? Even the expert, who programmed the user interface, does not know this! Mental model is inappropriate or missing! Semantics are missing (leads to gulf of execution) Result in trial-and-error

7. christof rezk-salama, institute for vision and graphics, university of siegen Application Abstraction Levels Low-Level Parameters (Color Table) High-Level Parameters (Primitive Shapes) Semantic Level Visibility Sharpness Contrast User All previous approaches aim at reducing the complexity, the degrees of freedom. None of the prevous approaches tries to provide an appropriate mental model!

8. christof rezk-salama, institute for vision and graphics, university of siegen Semantic Models Restrict ourselves to one specific application scenario. Example: CT angiography from neuroradiology The visualization task will be performed manually for multiple data sets. Visualization expert and medical doctor! Evaluate statistical information about the results: Which parameter modifications are necessary to make the blood vessels sharper? Use dimensionality reduction (PCA) to create a semantic model

9. christof rezk-salama, institute for vision and graphics, university of siegen Bone Step 1: Create a template for the TF Brain/Soft TissueSkin/CavitiesBlood vessels Developing a Semantic Model

10. christof rezk-salama, institute for vision and graphics, university of siegen Step 2: Adapt the template to reference data Developing a Semantic Model

11. christof rezk-salama, institute for vision and graphics, university of siegen Step 2: Adapt the template to reference data Developing a Semantic Model

12. christof rezk-salama, institute for vision and graphics, university of siegen Step 2: Adapt the template to reference data Developing a Semantic Model Step 3: Dimensionality reduction Reference Transfer Functions Principal Component Analysis Semantics Semantic Model

13. christof rezk-salama, institute for vision and graphics, university of siegen High-Level User Interface High-Level Control Transfer FunctionSemantic Model

14. christof rezk-salama, institute for vision and graphics, university of siegen Semantic Model

15. christof rezk-salama, institute for vision and graphics, university of siegen Prototype Implementation Applicable to anything that can be described by a parameter vector Take care of the scale! PCA for entire parameter vector is not appropriate Small details might be missed Our solution: Split transfer function into entities (=structures, groups of primitives with same scale) Perform PCA separately for each entity Reassemble the transfer function from the different entities

16. christof rezk-salama, institute for vision and graphics, university of siegen Results CTA: intracranial aneurysms: 512 x 512 x {120-160} @12bit, 100ml non-ionic contrast dye 20 data sets for training / 5 data sets for evaluation MR brain surgery: 256 x 256 x {150-200} @12bit (noisy, lower dynamic range ~10bit) 10 data sets Evaluation of the model: Analytically: Stability of the eigenvectors (dot product > 0.9) Stable for >12 data sets (regardless of individual choice) User Study: Labels removed from the user interface Most semantics were correctly identified by non-expert users

17. christof rezk-salama, institute for vision and graphics, university of siegen Conclusion User Interface Design Strategies: Reducing DOF is not enough. Good user interfaces must provide an appropriate mental model Not an attempt to create a single user interfaces for any visualization tasks Create semantic models for examination tasks as specific as necessary Building block for software assistants for medical diagnosis and therapy planning

18. christof rezk-salama, institute for vision and graphics, university of siegen Acknowledgements Bernd Tomandl MD, Neuroradiologie, Bremen Christopher Nimsky MD, Neurochirurgie, Erlangen