NA-MIC/Projects/Structural/Segmentation/Statistical PDE methods for Segmentation

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

Code

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

Links:

• Stony Brook Summary Page