Visualization of volumetric multicomponent data sets is a high-dimensional problem, especially for color data. Medical 3D ultrasound (US) technology has rapidly advanced during the last few decades and scanners can now generate joint 3D scans of tissues (B-mode) and blood flow (power or color Doppler) in real time. Renderings of such data sets have to comprehensively convey both the relevant structures of the tissues that form the context for blood flow, as well as the distribution of blood flow itself. The narrow field of view in US data, which is often used to make real-time imaging possible, complicates volume exploration since only parts of organs are usually visible; that is, clearly defined anatomical landmarks are scarce. In addition, the noisy nature and low signal-to-contrast ratio of US data make effective visualization a challenge, whereby there are currently no convincing solutions for combined US B-mode and color Doppler data rendering. Therefore, displaying 2D slices out of the 3D data is still often the preferred visualization method. We present new combinations of photorealistic and nonphotorealistic rendering strategies for combined visualization of B-mode and color Doppler data, which are straightforward to implement, flexible, and suited for a wide range of US applications.

Visualization of medical 3D data is a complex problem, since the raw data is often unsuitable for standard techniques like Direct Volume Rendering. Some kind of pre-treatment is necessary, usually segmentation of the structures of interest, which in turn is a difficult task. Most segmentation techniques yield a model without indicating any uncertainty. Visualization then can be misleading, especially if the original data is of poor contrast. We address this dilemma proposing a geometric approach based on distance on image manifolds and an alternative approach based on nonlinear diffusion. An effective algorithm solving Hamilton-Jacobi equations allows for computing a distance function for 2D and 3D manifolds at interactive rates. An efficient implementation of a semi-implicit operator splitting scheme accomplishes interactivity for the diffusion-based strategy. We establish a model which incorporates local information about its reliability and can be visualized with standard techniques. When interpreting the result of the segmentation in a diagnostic setting, this information is of utmost importance.