1. WP3: Visualization services
NOVA Kickoff Meeting
DESY, September 19, 2016
WP3: Visualization services
N. Tan Jerome, S. Chilingaryan

2. Current Status (3D online previewer)
Direct volume rendering.
Surface rendering (Illumination model).
Multimodality visualization.
Grey value threshold.
Slice view in x-y-z direction.
Motivation:
Web based
Address client resources -> mobile -> laptop -> desktop
Nicholas Tan Jerome – Visualization Services
Prof. Dr. Max Mustermann | Musterfakultät

3. Wasp Dataset (Entomology)
Demo
Wasp Dataset (Entomology)
Direct volume rendering.
Surface rendering.
(grey value )
Breast Dataset (Human Biology)
Multimodality visualization.
HSV-approach.
Grey value threshold
X-y-z slice direction
Add videos.
Nicholas Tan Jerome – Visualization Services
Prof. Dr. Max Mustermann | Musterfakultät

4. Multimodality visualization.
Work Packages
Data management system that manages the preprocessing of the visualization input data structure.
Automatic adaptation on visualization parameter, i.e. opacity, grey value threshold, transfer function, ROI, orientation, …).
Multimodality visualization.
Using segmented dataset as mask to show partial visualization.
Feature extraction from segmented dataset.
X-ray interferometry produces phase-contrast imaging.
Nicholas Tan Jerome – Visualization Services
Prof. Dr. Max Mustermann | Musterfakultät

5. Data Management Multiple cache level. Progressive loading.
2D slicemap (Congote et. al., 2011)
2D input
Multiple cache level.
Progressive loading.
Adaptive display.
Interactive zooming.
Nicholas Tan Jerome – Visualization Services
Prof. Dr. Max Mustermann | Musterfakultät

6. Adaptive Visualization Parameters
Visual Quality.
Background removal by employing a automatic/semi automatic thresholding algorithms.
Show high quality when no events triggered.
Histogram analysis (addresses the data sparsity as well).
Performance.
Empty space skipping.
Stop ray casting after 5 seconds idle time.
Adapt ray casting step according to camera movement.
Adapting performance quality depending on the hardware specs, i.e. screen size, GPU performance, smaller slice map resolution, better filter selection.
e.g. otsu, li, yen [21]
Otsu
The algorithm assumes that the image contains two classes of pixels following bi-modal histogram.
In Otsu's method we exhaustively search for the threshold that minimizes the intra-class variance (the variance within the class), defined as a weighted sum of variances of the two classes:
Adapting performance quality depending on the hardware specs,
i.e. screen size,
GPU performance -> reducing steps,
smaller slicemap resolution,
better filter selection.
Nicholas Tan Jerome – Visualization Services
Prof. Dr. Max Mustermann | Musterfakultät

7. Multimodality Visualization
HSV integration approach.
Convert RGB to HSV.
Applicable for phase-contrast imaging.
R channel (Primary modal)
G channel (Secondary modal)
B channel (Structure information)
Ask for phase-contrast imaging?? Or anything?
Nicholas Tan Jerome – Visualization Services
Prof. Dr. Max Mustermann | Musterfakultät

8. Visualization of Segmented Layers
Overlay segmented dataset on top of the structure dataset. (Highlight the segmented section).
Annotation and labelling (metadata stored in database).
Multiple layers with different colors.
Add/remove specific layers.
Nicholas Tan Jerome – Visualization Services

9. Roadmap After 6 months After 1 year After 2 years
Prototype data management system (web portal)
Visualization on segmented layers.
Enable annotation.
After 1 year
Data caching and backend performance optimization.
Improve visualization performance. Automatic parameter settings.
Background removal.
Histogram analysis.
After 2 years
Evaluate multimodality visualization approach in tomographic application.
Nicholas Tan Jerome – Visualization Services

10. Thanks.
Nicholas Tan Jerome – Visualization Services