This research focuses on performing interactive, real-time volume raycasting in a large clustered graphics environment
using custom GPU shaders for composite volume raycasting with trilinear interpolation. Working in this type of
environment presents unique challenges due to the distributed nature, and inherently required synchronization of data
and operations across the cluster. Invoking custom vertex and fragment shaders in a non-thread-safe manner becomes
increasingly complex in a large clustered graphics environment. Through use of an abstraction layer, all rendering
contexts are split-up with no changes to the volume raycasting core. Therefore, the volume raycasting core is completely
transparent from the computing platform. The application was tested on a 6-wall immersive VR system with 96 graphics
contexts coming from 48 cluster nodes. Interactive framerates of 60 frames per second were produced on 512x512x100
volumes, and an average of 30 frames per second for a 512x512x1000 volume. The use of custom configuration files
allows the same code to be highly scalable from a single screen VR system to a fully immersive 6-sided wall VR system.
Through the code abstraction, the same volume raycasting core can be implemented on any type of computing platform
including desktop and mobile.
This paper presents a probabilistic segmentation process developed using the selection process from the Simulated
Annealing optimization algorithm as a foundation. This process allows pixels to be segmented based on a probability
selection process. An automated seed and search region selection processes multiple image slices automatically as an
object's size, shape, and location changes between subsequent slices. Apart from the first slice in the dataset, where the
user manually selects the seed and search region for segmentation, the method performs automatically for all other
slices. From the test cases, the automated seed selection process was efficient in searching for new seed locations, as the
object changed size, location, and orientation in each slice of the study. Segmentation results from both algorithms
showed success in segmenting the tumor from nine of the ten CT datasets with less than 17% false positive errors and
seven test cases with less than 20% false negative errors. Statistical testing of the results showed a high repeatability
factor, with low values of inter- and intra-user variability. Furthermore, the method requires information from only a
two-dimensional image data at a time to accommodate performance on a regular personal computer.