Automatic characterization of cross-sectional coated particle nuclear fuel using greedy coupled Bayesian snakes
Jeffery R. Price, Deniz Aykac, John D. Hunn, Andrew Keith Kercher · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
We describe new image analysis developments in support of the U.S. Department of Energy's (DOE) Advanced Gas Reactor (AGR) Fuel Development and Qualification Program. We previously reported a non-iterative, Bayesian approach for locating the boundaries of different particle layers in cross-sectional imagery. That method, however, had to be initialized by manual preprocessing where a user must select two points in each image, one indicating the particle center and the other indicating the first layer interface. Here, we describe a technique designed to eliminate the manual preprocessing and provide full automation. With a low resolution image, we use "EdgeFlow" to approximate the layer boundaries with circular templates. Multiple snakes are initialized to these circles and deformed using a greedy Bayesian strategy that incorporates coupling terms as well as a priori information on the layer thicknesses and relative contrast. We show results indicating the effectiveness of the proposed method.