A crucial step in volume rendering is the design of transfer functions that will highlight those aspects of the volume data that are of interest to the user. For many applications, boundaries carry most of the relevant information. Reliable detection of boundaries is often hampered by limitations of the imaging process, such as blurring and noise. We present a method to identify the materials that form the boundaries. These materials are then used in a new domain that facilitates interactive and semiautomatic design of appropriate transfer functions. We also show how the obtained boundary information can be used in region-growing-based segmentation.

High-angular resolution diffusion imaging (HARDI) is a diffusion weighted MRI technique that overcomes some of the decisive limitations of its predecessor, diffusion tensor imaging (DTI), in the areas of composite nerve fiber structure. Despite its advantages, HARDI raises several issues: complex modeling of the data, nonintuitive and computationally demanding visualization, inability to interactively explore and transform the data, etc. To overcome these drawbacks, we present a novel, multifield visualization framework that adopts the benefits of both DTI and HARDI. By applying a classification scheme based on HARDI anisotropy measures, the most suitable model per imaging voxel is automatically chosen. This classification allows simplification of the data in areas with single fiber bundle coherence. To accomplish fast and interactive visualization for both HARDI and DTI modalities, we exploit the capabilities of modern GPUs for glyph rendering and adopt DTI fiber tracking in suitable regions. The resulting framework, allows user-friendly data exploration of fused HARDI and DTI data. Many incorporated features such as sharpening, normalization, maxima enhancement and different types of color coding of the HARDI glyphs, simplify the data and enhance its features. We provide a qualitative user evaluation that shows the potentials of our visualization tools in several HARDI applications.

Abstract. We present a new method to visualize virtual endoscopic views. We propose to flatten the organ by the direct projection of the surface onto a set of cylinders. Two sampling strategies are presented and the introduced distortions are studied. A non-photorealistic technique is presented to enhance the perception of the images. Finally, an approximate but real-time endoscopic fly-through is possible by using the data obtained by the projection technique.

Summary: Background: CT colonography was found to be sensitive and specific for detection of colonic polyps and colorectal cancer (CRC). Depending on the software used, CT colonography requires a certain amount of operator interaction, which limits it's widespread usage. The goal of this papers is to present two novel automated techniques for displaying CT colonography: virtual dissection and automated colonic polyp detection. 
Methods: Virtual dissection refers to a technique where the entire colon is virtually stretched and flattened thus simulating the view on the pathologist's table. 
Colonic folds show a ‘global outward bulging of the contour’, whereas colonic polyps exhibit the inverse (‘local inward bulging’). This feature is used to map areas of ‘local inward bulging’ with colours on 3D reconstructions. A cadaveric phantom with 13 artificially inserted polyps was used for validation of both techniques. 
Results: On virtual dissection all 13 inserted polyps could be identified. They appeared either as bumps or as local broadening of colonic folds. In addition, the automated colonic polyp detection algorithm was able to tag all polyps. Only 10 min of operator interaction were necessary for both techniques. 
Conclusions: Virtual dissection overcomes the shortcomings of CT colonography, and automated colonic polyp detection establishes a roadmap of the polyps.

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