An automated segmentation for nickel-based superalloy

We investigate the automated segmentation of microstructures of a nickel-based superalloy using digital microscopy data. We study the combination of a region merging segmentation method called the stabilized inverse diffusion equation (SIDE), and a stochastic segmentation method, the expectation-maximization/maximization of the posterior marginals (EM/MPM) algorithm. We use the SIDE algorithm to segment the grain boundaries and we use the EM/MPM algorithm to classify two phases of the material within each grain. Experimental results demonstrate the effectiveness of our approach.