Projects:StatisticalSegmentationSlicer2

Statistical Segmentation Slicer 2

Back to NA-MIC_Collaborations, Georgia Tech Algorithms

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

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]

Key Investigators:

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

Links:

• Georgia Tech Algorithms
• NA-MIC Collaborations