Book Description

The visualization of volume data is a fundamental component in the medical domain. Volume data is used in the clinical work-flow to diagnose patients and is therefore of uttermost importance. The amount of data is rapidly increasing as sensors, such as computed tomography scanners, become capable of measuring more details and gathering more data over time. Unfortunately, the increasing amount of data makes it computationally challenging to interactively apply high quality methods to increase shape and depth perception. Furthermore, methods for exploring volume data has mostly been designed for experts, which prohibits novice users from exploring volume data. This thesis aims to address these challenges by introducing efficient methods for enhancing salient features through high quality illumination as well as methods for intuitive volume data exploration. Humans are interpreting the world around them by observing how light interacts with objects. Shadows enable us to better determine distances while shifts in color enable us to better distinguish objects and identify their shape. These concepts are also applicable to computer generated content. The perception in volume data visualization can therefore be improved by simulating real-world light interaction. This thesis presents efficient methods that are capable of interactively simulating realistic light propagation in volume data. In particular, this work shows how a multi-resolution grid can be used to encode the attenuation of light from all directions using spherical harmonics and thereby enable advanced interactive dynamic light configurations. Two methods are also presented that allow photon mapping calculations to be focused on visually changing areas.The results demonstrate that photon mapping can be used in interactive volume visualization for both static and time-varying volume data. Efficient and intuitive exploration of volume data requires methods that are easy to use and reflect the objects that were measured. A value that has been collected by a sensor commonly represents the material existing within a small neighborhood around a location. Recreating the original materials is difficult since the value represents a mixture of them. This is referred to as the partial-volume problem. A method is presented that derives knowledge from the user in order to reconstruct the original materials in a way which is more in line with what the user would expect. Sharp boundaries are visualized where the certainty is high while uncertain areas are visualized with fuzzy boundaries. The volume exploration process of mapping data values to optical properties through the transfer function has traditionally been complex and performed by expert users. A study at a science center showed that visitors favor the presented dynamic gallery method compared to the most commonly used transfer function editor.