Projects:StatisticalSegmentationSlicer2

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Statistical Segmentation Slicer 2

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE frameowrk.

Description

We developped flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows.

Completed

• A statistically based flow for image segmentation, using Fast Marching

Figure 1:Screenshot from the Slicer Fast Marching module

• The code has been integrated into the Slicer
• A user-oriented tutorial for the Fast Marching algorithm is available at:Slicer Module Tutorial

Improvements

Improvements over the original method are here.

Key Investigators

• Georgia Tech Algorithms: Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum

Publications

In print

• NA-MIC Publications Database on Statistical/PDE Methods using Fast Marching for Segmentation

Note: Best student presentation in image segmentation award[1]