1. Hypothesis on applied visualization - what to measure?
I'm beginning my research which entails applying visualization to a
complex model of biomedical phenomena. I'll be developing the model,
as well as a unique (I hope) visualization paradigm to present
my results. Note that I'm not doing volume visualization of some
radiological results. I'm presenting simulation results - somewhat
like CFD (computational fluid dynamics) visualization in aerospace.
My question itself is hard to state. A hypothesis which emphasizes the
visualization over the model is hard to define, and harder to measure.
If my hypothesis is on the lines of:
"Visualization paradigm X improves comprehension of model Y"
then, once I develop paradigm X, how do I prove that comprehension was
increased? I'd like to stay away from some subjective evaluation
of 10 expert's opinions. This is engineering.
On the other hand, I could emphasize that the model is novel:
"Model Y successfully predicts (to a measurable point) the dynamics of
the phenomena (system) - visualization used to present the results"
but this hypothesis emphasises the model, not the research in
I realize that in Computer Science or Electrical Engineering a novel,
general visualization aspect or environment applicable to many domains
would do as a research hypothesis, but as biomedical engineering
specifically, my research should be more specific and less general.
Someone researching visualization applied to CFD - do they somehow
"prove" that their visualization paradigms are "best" for CFD solution analysis?
If they just show that the CFD results are visible in the paradigm, what would
they be proving?
I hope my confusion didn't muddle my question - there is not one answer,
but as many as there are dissertation committees. But I'd like
some opinions, and perhaps some references to similar applied scientific
visualization dissertations if anyone knows of some.
Brandon S. Dewberry Biomedical Engineering Department
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