Machine Learning evaluation of microscopy image segmentation methods: The case of Gaussian Mixture Models

Multiphase materials are encountered in several areas of science and technology. Their properties are determined by the fraction of the phases (material compounds) constituting the composite material. Therefore, the quantitative characterization of phase fractions is highly demanded and has been the subject of extensive studies. To this end, a widely used technique is the segmentation of top-down back-scattered electron SEM (BSE-SEM) images given that different phases are depicted with pixel collections of different luminosity. Gaussian mixture models (GMM) are one the most popular and easily implemented methods to segment the BSE-SEM images through the deconvolution of their histograms. However, the accuracy and the limitations of their application have not been fully investigated. The aim of this paper is to design a neural-network approach to fill this gap and provide a fast tool for the automatic evaluation of the accuracy of GMM predictions for all material phases based on the inspection of the measured SEM image histogram alone. The proposed tool facilitates the decision-making process of an SEM user concerning the optimum choice of a segmentation method.