GMM- based image segmentation approach for SOFC microstructure characterization

In order to accurately evaluate the microstructure parameters of Solid Oxide Fuel Cell (SOFC) electrode, this paper presents a novel image segmentation method based on Gaussian Mixture Model (GMM) to identify the three phases of electrode optical microscope image. Firstly, the spatial neighbor information is introduced into EM optimization algorithm to constrain the weighted probability distribution of each pixel. Secondly, for uncertain points whose probabilities of two components are close, the probability distribution of them is adjusted according to quantum-inspired adaptive weight. The experimental results show that the proposed method is effective to separate the three phases of electrode, and provide reliable data support for SOFC 3D reconstruction.