High-Level User Interfaces for Transfer Function Design with Semantics - PowerPoint PPT Presentation

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High-Level User Interfaces for Transfer Function Design with Semantics

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Title: High-Level User Interfaces for Transfer Function Design with Semantics


1
High-Level User Interfacesfor Transfer Function
Design with Semantics
  • Christof Rezk Salama (Univ. Siegen , Germany)
  • Maik Keller (Univ. Siegen, Germany)
  • Peter Kohlmann (TU Vienna, Austria)

2
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
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
The Mental Model
Example taken from Donald A. Norman The
Psychology of Everyday Things
5
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

2D Transfer Functions
1D Transfer Functions
6
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, doesnot know this!
  • Mental model is inappropriate or missing!
  • Semantics are missing (leads to gulf of
    execution)
  • Result in trial-and-error

7
Abstraction Levels
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
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
Developing a Semantic Model
Step 1 Create a template for the TF
10
Developing a Semantic Model
Step 2 Adapt the template to reference data
11
Developing a Semantic Model
Step 2 Adapt the template to reference data
12
Developing a Semantic Model
Step 2 Adapt the template to reference data
Step 3 Dimensionality reduction
Reference Transfer Functions
Semantic Model
13
Semantic Model
High-Level User Interface
Transfer Function
Semantic Model
14
Semantic Model
15
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
Results
  • CTA intracranial aneurysms
  • 512 x 512 x 120-160 _at_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 _at_12bit (noisy, lower
    dynamic range 10bit)
  • 10 data sets
  • Evaluation of the model
  • Analytically Stability of the eigenvectors (dot
    product gt 0.9)
  • Stable for gt12 data sets (regardless of
    individual choice)
  • User Study Labels removed from the user
    interface
  • Most semantics were correctly identified by
    non-expert users

17
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
Acknowledgements
  • Bernd Tomandl MD, Neuroradiologie, Bremen
  • Christopher Nimsky MD, Neurochirurgie, Erlangen
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