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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. - PowerPoint PPT Presentation
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institute for vision and graphics university of siegen, germany
High-Level User Interfacesfor Transfer Function Design with Semantics
High-Level User Interfacesfor Transfer Function Design with SemanticsChristof Rezk Salama (Univ. Siegen , Germany)
Maik Keller (Univ. Siegen, Germany)
Peter Kohlmann (TU Vienna, Austria)
christof rezk-salama, institute for vision and graphics, university of siegen
Volume VisualizationVolume Visualization
Volume visualization techniques are mature from the technical point of view.
Real-time volume graphics on commodity PC hardwareMultidimensional transfer functions/classificationGradient estimation and local illumination on-the-flyMemory management and compression for large volumesEven 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“
christof rezk-salama, institute for vision and graphics, university of siegen
QuestionsQuestions
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?
christof rezk-salama, institute for vision and graphics, university of siegen
The Mental ModelThe Mental ModelExample taken from: Donald A. Norman
The Psychology of Everyday Things
christof rezk-salama, institute for vision and graphics, university of siegen
Volume VisualizationVolume 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
christof rezk-salama, institute for vision and graphics, university of siegen
User IntentionUser IntentionExamples:
„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
christof rezk-salama, institute for vision and graphics, university of siegen
Application
Abstraction LevelsAbstraction Levels
Low-Level Parameters(Color Table)
High-Level Parameters(Primitive Shapes)
Semantic LevelVisibilitySharpnessContrast
User
All previous approaches aim at reducing the complexity, the degrees of freedom.
None of the prevous approachestries to provide an appropriate mental model!
christof rezk-salama, institute for vision and graphics, university of siegen
Semantic ModelsSemantic ModelsRestrict 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
christof rezk-salama, institute for vision and graphics, university of siegen
Bone
Step 1: Create a template for the TF
Brain/Soft Tissue Skin/Cavities Blood vessels
Developing a Semantic ModelDeveloping a Semantic Model
christof rezk-salama, institute for vision and graphics, university of siegen
Step 2: Adapt the template to reference data
Developing a Semantic ModelDeveloping a Semantic Model
christof rezk-salama, institute for vision and graphics, university of siegen
Step 2: Adapt the template to reference data
Developing a Semantic ModelDeveloping a Semantic Model
christof rezk-salama, institute for vision and graphics, university of siegen
Step 2: Adapt the template to reference data
Developing a Semantic ModelDeveloping a Semantic Model
Step 3: Dimensionality reduction
ReferenceTransfer Functions
Principal Component Analysis
Semantics
Semantic Model
christof rezk-salama, institute for vision and graphics, university of siegen
High-Level User Interface
High-Level Control
Transfer Function Semantic Model
Semantic ModelSemantic Model
christof rezk-salama, institute for vision and graphics, university of siegen
Semantic ModelSemantic Model
christof rezk-salama, institute for vision and graphics, university of siegen
Prototype ImplementationPrototype Implementation
Applicable to „anything that can be described by a parameter vector“
Take care of the scale!PCA for entire parameter vector is not appropriateSmall 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
christof rezk-salama, institute for vision and graphics, university of siegen
ResultsResultsCTA: intracranial aneurysms:
512 x 512 x {120-160} @12bit, 100ml non-ionic contrast dye20 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 interfaceMost semantics were correctly identified by non-expert users
christof rezk-salama, institute for vision and graphics, university of siegen
ConclusionConclusionUser 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 tasksCreate semantic models for examination tasks as specific as necessaryBuilding block for software assistants for medical diagnosis and therapy planning
christof rezk-salama, institute for vision and graphics, university of siegen
AcknowledgementsAcknowledgements
Bernd Tomandl MD, Neuroradiologie, BremenChristopher Nimsky MD, Neurochirurgie, Erlangen