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Research in medical visualization lead to a remarkable collection of algorithms for efficiently exploring medical imaging data, such as CT, MRI and DTI. However, widespread use of such algorithms requires careful parameterization, integration of individual algorithms in solutions for real-world problems in diagnosis, treatment planning and intraoperative navigation. In the field of HCI, input devices, interaction techniques as well as a process for achieving usable, useful, and attractive user interfaces are explored. Findings from HCI may serve as a starting point to significantly improve visual computing solutions in medical diagnosis and treatment. We discuss general issues, such as input devices for medical visualization, and selected examples.

Illustrative techniques are a new and exciting direction in visualization research. Traditional techniques which have been used by scientific illustrators for centuries are re-examined under the light of modern computer technology. In this paper, we discuss the use of the focus+context concept for the illustrative visualization of volumetric data. We give an overview of the state-of-the-art and discuss recent approaches which employ this concept in novel ways.

Computer support for intervention planning is often a two-stage process: In a first stage, the relevant segmentation target structures are identified and delineated. In a second stage, image analysis results are employed for the actual planning process. In the first stage, model-based segmentation techniques are often used to reduce the interaction effort and increase the reproducibility. There is a similar argument to employ model-based techniques for the visualization as well. With increasingly more visualization options, users have many parameters to adjust in order to generate expressive visualizations. Surface models may be smoothed with a variety of techniques and parameters. Surface visualization and illustrative rendering techniques are controlled by a large set of additional parameters. Although interactive 3d visualizations should be flexible and support individual planning tasks, appropriate selection of visualization techniques and presets for their parameters is needed. In this chapter, we discuss this kind of visualization support. We refer to model-based visualization to denote the selection and parameterization of visualization techniques based on 'a priori knowledge concerning visual perception, shapes of anatomical objects and intervention planning tasks.