Expressive Visualization of Volumetric Data

The main goal of the research project are automatic computer assisted expressive visualizations of complex volumetric data. Guidelines used by artists for handcrafted medical and technical illustrations will be carried over to computer algorithms to automize the design process of expressive images. The proposed visualization techniques enhance the most prominent information within the volume data, to maximize the visual information on the resulting image. The prominence of a particular feature is determined by an additional importance information, which is assigned by automatic feature classification techniques. The importance information enables to detect image regions, where features of high importance (focus) would be occluded by less important structures (context). The context information is therefore automatically suppressed in order to enhance more prominent information behind it. For the feature suppression various representation techniques can be applied to efficiently convey the shape of a structure within the volumetric data, but they take-up only a small portion on the image space. A feature can be suppressed globally, or only on the occluding region. This has the advantage that parts of the feature will be not unnecesarilly supressed in areas where no occlusion occurs. Therefore we propose various visualization techniques, such as automatic cut-away and ghosted views, exploded views, or section views. Such techniques are very often used in technical or medical illustrations because of their effective presentation of information. The importance information additionally enables to evaluate the quality of the presented information on the image for each viewpoint. This is called viewpoint entropy. Thus it is possible to automatically select the viewpoint with highest information content. Furthermore the importance factor in combination with the viewpoint entropy can be incorporated in an automatic transfer function specification. New expressive visualizations will be evaluated through a user study. Finally we will apply expressive visualizations for challenging tasks in medical visualization to, e.g., speed-up the diagnosis and facilitate operation planning.

The main goal of the research project are automatic computer assisted expressive visualizations of complex volumetric data. Guidelines used by artists for handcrafted medical and technical illustrations will be carried over to computer algorithms to automize the design process of expressive images. The proposed visualization techniques enhance the most prominent information within the volume data, to maximize the visual information on the resulting image. The prominence of a particular feature is determined by an additional importance information, which is assigned by automatic feature classification techniques. The importance information enables to detect image regions, where features of high importance (focus) would be occluded by less important structures (context). The context information is therefore automatically suppressed in order to enhance more prominent information behind it. For the feature suppression various representation techniques can be applied to efficiently convey the shape of a structure within the volumetric data, but they take-up only a small portion on the image space. A feature can be suppressed globally, or only on the occluding region. This has the advantage that parts of the feature will be not unnecesarilly supressed in areas where no occlusion occurs. Therefore we propose various visualization techniques, such as automatic cut-away and ghosted views, exploded views, or section views. Such techniques are very often used in technical or medical illustrations because of their effective presentation of information. The importance information additionally enables to evaluate the quality of the presented information on the image for each viewpoint. This is called viewpoint entropy. Thus it is possible to automatically select the viewpoint with highest information content. Furthermore the importance factor in combination with the viewpoint entropy can be incorporated in an automatic transfer function specification. New expressive visualizations will be evaluated through a user study. Finally we will apply expressive visualizations for challenging tasks in medical visualization to, e.g., speed-up the diagnosis and facilitate operation planning.