Visualization consists of the transformation and manipulation of datasets, to detect structure in them. This is done by object segmentation, quantification and feature tracking.

1. Object Segmentation Datasets are transformed to alternate spaces to detect inherent structures in apprently amorphous data. Object segmentation is used to identify and extract structures. In object segmentation, objects are classified based on connectivity. Color can be assigned to visualize their properties.

   Rendering with standard isosurface, structures are not differentiable.

   

   Segmented objects are colored with linear colormap based on specific property.

   Mass Volume Local Maximum

   Other Applications:

   Object segmentation technique has been applied to other domain. The following image is a primary result obtained by applying object segmentation technique to the CAT scan data of a suitcase. A can in the suitcase is extracted.

   

2. Ellipsoidal Fitting Object attributes, such as mass, volume, second order moments, are visualized with ellipsoidal fitting.

   

3. Feature Tracking Tracking is the correlattion of extracted objects from one dataset to the extracted objects in a subsequent dataset. This allows us to track the split and mergers of different objects over time. An example :

   
   Click on the relevant part to see animation.

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   Maintained by

   Simon Xin Wang (xswang@vizlab.rutgers.edu)
   Prof. Deborah Silver (silver@vizlab.rutgers.edu)

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