Two methods of random seed generation to avoid over-segmentation with stochastic watershed: application to nuclear fuel micrographs

A stochastic version of the watershed algorithm is obtained by choosing randomly in the image the seeds from which the watershed regions are grown. The output of the procedure is a probability density function corresponding to the probability that each pixel belongs to a boundary. In the present paper, two stochastic seed-generation processes are explored to avoid over-segmentation. The first is a non-uniform Poisson process, the density of which is optimized on the basis of opening granulometry. The second process positions the seeds randomly within disks centred on the maxima of a distance map. The two methods are applied to characterize the grain structure of nuclear fuel pellets. Estimators are proposed for the total edge length and grain number per unit area, L-A and N-A, which take advantage of the probabilistic nature of the probability density function and do not require segmentation.