Petersch, B.Serrano-Serrano, O.Hönigmann, D.Beatriz Sousa Santos and Thomas Ertl and Ken Joy2014-01-312014-01-3120063-905673-31-21727-5296https://doi.org/10.2312/VisSym/EuroVis06/331-338Visualization 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.Categories and Subject Descriptors (according to ACM CCS): I.4.6 [IMAGE PROCESSING AND COMPUTER VISION]: Segmentation I.3.7 [COMPUTER GRAPHICS]: Three-Dimensional Graphics and Realism - Volume Rendering I.3.8 [COMPUTER GRAPHICS]: Applications