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• W2122000670 abstract "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." @default.
• W2122000670 created "2016-06-24" @default.
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• W2122000670 date "2006-05-08" @default.
• W2122000670 modified "2023-09-26" @default.
• W2122000670 title "3D soft segmentation and visualization of medical data based on nonlinear diffusion and distance functions" @default.
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• W2122000670 doi "https://doi.org/10.2312/vissym/eurovis06/331-338" @default.
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