High level user interfaces for transfer function design with semantics
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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). Volume Visualization. Volume visualization techniques are mature from the technical point of view.

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High-Level User Interfaces for Transfer Function Design with Semantics

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High level user interfaces for transfer function design with semantics

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)


Volume visualization

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, he‘ll probably say „yes“.

    • If you ask the users, they will most likely say „no“


Questions

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?


The mental model

The Mental Model

Example taken from:

Donald A. Norman

The Psychology of Everyday Things


Volume visualization1

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


User intention

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


Abstraction levels

User

Semantic Level

Visibility

Sharpness

Contrast

High-Level Parameters

(Primitive Shapes)

Low-Level Parameters

(Color Table)

Application

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!


Semantic models

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 expertandmedical 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


Developing a semantic model

Bone

Brain/Soft Tissue

Skin/Cavities

Blood vessels

Developing a Semantic Model

Step 1: Create a template for the TF


Developing a semantic model1

Developing a Semantic Model

Step 2: Adapt the template to reference data


Developing a semantic model2

Developing a Semantic Model

Step 2: Adapt the template to reference data


Developing a semantic model3

Principal

Component

Analysis

Semantics

Developing a Semantic Model

Step 3: Dimensionality reduction

Step 2: Adapt the template to reference data

Reference

Transfer Functions

Semantic Model


Semantic model

High-Level Control

Semantic Model

High-Level User Interface

Transfer Function

Semantic Model


Semantic model1

Semantic Model


Prototype implementation

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


Results

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


Conclusion

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


Acknowledgements

Acknowledgements

  • Bernd Tomandl MD, Neuroradiologie, Bremen

  • Christopher Nimsky MD, Neurochirurgie, Erlangen


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