SP2.3:
UI and VR Based Visualization
Partners: TU Delft, VU, CWI
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Ongoing Activities and progress
Collaboration Highlight with SP 1.6 DUTELLA
R. van Liere
April 7th, 2006
SP 2.3 people
4 PhD students:
Broersen, Burakiew, Kruszynski
van der Schaaf
(CWI)
(VU)
3 PD:
Botha, Koutek
de Leeuw
(TUD)
(CWI)
4 supervision:
van Liere
Post, Jansen
Bal
(CWI)
(TUD)
(VU)
SP2.3 ongoing activities
Multi-spectral visualization
Particle visualization
Confocal Cell Imaging
Volume measuring
Medical Imaging
Virtual Reality on the GRID
Distributed Scene Graphs
SP 1.6
SP 1.6
SP 2.1
SP 1.4
SP 3.1
SP 3.1
SP 2.3 status
25 international publications
2 spin-offs
Foldyne (TU Delft)
Personal Space Technologies (CWI)
Projected output
4 PhD thesis
At least 2 packages in PoC
Collaboration SP 1.6 DUTELLA
Prof Ron Heeren (ALMOF)
Topic: Mass Spectrometry for molecular imaging
Motivation: need for better MS analysis tools
Visualization Topics:
Multi-spectral data visualization
In-silico mass spectrometry
Envisioned output:
GRID enabled toolbox for MS analysis
Applications according to VL-e methodology
Problem: aligning multi-spectral
data cubes
Multi-spectral data cube: 256x256x65k
Multiple data cubes
±100 cubes in mosaic
Current procedure: manual alignment on
pixel values
Our novel approach
Idea: Align spectral features in
adjacent samples
Approach:
Compute spectral features using PCA
For each feature, find a most optimal
spatial alignment of the feature
The overall spatial alignment is optimal
for all features
MS beelden dijbeen muis
First Spectral Feature =
Principal Component1
Second Spectral Feature
Principal Component2
Minima landscape
use the
combination
of 2 local
minima
Minimization map of 1st
feature
Minimization map of 2nd
feature
Impact ? Generic ? GRID?
Faster, unsupervised objective
reproducible alignment combined
with VL inspection tools for SP1.6
Method can also be applied to multispectral data cubes from other types
of microscopes/telescopes.
Data-cube:256x256x65K. 100 cubes.
Alignment:15min in Matlab.
Combinations: (100 2) * 15
Problem: Meaningful ion dynamics
Ion clouds: ~50k ions x 1M steps
Current visualizations are low level, eg.:
But how about:
Intra ion-cluster interactions and their causes
Intra ion-cluster interactions?
Our novel approach
Idea: simplify images with
Statistical parameterized icons
Semantic camera control
Approach:
Parameterized “comet-icons”
Camera motion relative to comet dynamics
Example: icons
Ions groups
Statistical ion properties of group
Ion density dynamics
Example: camera control
Trapping motion
Relative cyclotron frequency
Tracks of Frenet frames
Impact ? Generic ? GRID?
Improvement of mass accuracy
understanding/control leads to
enhanced protein ID in proteomics
Software framework is targeted
towards particle visualization.
Semantics of icons/cameras can be
added/changed/enhanced
Near-future: optimization of
simulation initial conditions
Final SP 2.3 comments
SP 2.3 is well on track
Projected output:
GRID enabled toolbox
Applications using toolbox
SP2 layer
SP1 layer
However: visualization PhDs are not
mass spectrometry scientists!