MSc Thesis: Status update #1
December 23, 2010 | Comments | Master Thesis, Medical Visualization
In the previous weeks / month I have finished my Literature study and started my actual MSc thesis project. The outcome of my literature study was that illustrative visualization will play a major role in medical visualization systems. In addition, the user should be in control of the system, and the system should be comprehensible. This requires comprehensible specification of illustrative styles, and a natural way of interaction which is customizable by the user.
However, even more important is usability of the system. Merely having these techniques available to the user is not enough. The best example is found in transfer function specification. Transfer function specification is a complex and cumbersome process, even with state-of-the-art methods. The idea to focus on comprehensible specification of illustrative styles is supported by the semantic layers [Rautek 07], and style transfer functions [Bruckner 07]. Both focus on comprehensible specification, semantic layers go as far as using semantic values in the natural language of the domain of the user.
Considering interaction with the application, most important is that interaction should feel natural, and should be customizable by the user. Two-handed interaction might be a solution to interacting with the data in a natural way. It is already natural to use both of our hands in a lot of situations in our every day life, so extending this to interaction with virtual data would seem logical to us. Considering customizability of interaction, this relates to our idea that the user should be the one controlling the system. One part of this control is specification of illustrative styles, the other part would be specification of the way of interaction. Again, our idea is supported by Rautek et al. in their work on interaction-dependent semantic layers [Rautek 08b].
The envisioned system
Our envisioned visualization system for anatomy education will make use of a high-resolution histological dataset [More]
Popularity: 50%