Context-Preserving Volumetric Data Set Exploration Using a 3D Painting Metaphor

نویسندگان

  • L. Faynshteyn
  • Tim McInerney
چکیده

This paper presents a technique for fast and intuitive visualization and exploration of unsegmented volumetric data sets. It combines a Maximum Intensity Difference Accumulation (MIDA) visualization algorithm with a 3D interaction model based on a painting metaphor. Under this model the Volume of Interest (VOI) is defined by “painting” an envelope enclosing features of interest. The key advantage of the model is the ability to interactively preview the effect of the painting operation before it is applied, allowing the user to both explore and paint in the volume space at the same time. The result is a flexible and intuitive technique that uses a single, consistent interaction style to achieve desired contextual views and perform several types of volume exploration and editing operations. Examples using several volumetric data sets and the results of a user study are presented to validate the effectiveness, flexibility and ease of use of the technique.

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تاریخ انتشار 2012