IDP: Visualization of Medical Cryosections for Augmented Reality

In the last years several datasets consisting of computed tomography (CT) images, magnetic resonance (MR) images and cryosections of the same person have become available for research. These datasets are known as visible human datasets. Cryosections are colour images that are obtained by freezing a cadaver, cutting it into thin slices and scanning it. Most work on medical visualization uses only grey values as the common imaging modalities like CT, MR and Ultrasound only provide one intensity value per voxel. As the cryosections provide colour images they can be used for advanced visualization of medical data e.g. for training. There are several challenges in visualizing volumetric data obtained from cryosections. The standard visualizations methods for grey-value images can not be easily applied to colour images as the concept of transfer function is much more complicated for colour images. When showing CT, MR and cryosections at the same time this is even more problematic as we have five colour values per voxel. Also the computational demands rise, as the dataset is much bigger than volumes containing only grey-values. In this project different known methods for visualization of 3D colour images will be implemented and extended. The main goal is to integrate the visualization into a medical Augmented Reality (AR) system that uses a video see-through head mounted display (HMD). A physical dummy that was build based on data from a visible human is available. For this application, AR will be used to allow a view into the dummy where different organs can be shown using the visualization methods that are developed in this project.