Projects:StatisticalSegmentationSlicer2

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Objective:

We want 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.

Progress:

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

Key Investigators:

Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum

References:

• Eric Pichon, Allen Tannenbaum, and Ron Kikinis. A statistically based surface evolution method for medical image segmentation: presentation and validation. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 2, pages 711-720, 2003. Note: Best student presentation in image segmentation award[1]

Links:

• Georgia Tech Algorithms
• NA-MIC Collaborations