Coarseness-entropy based Gaussian Mixture Model for SOFC image segmentation

For the three-phase identification of Solid Oxide Fuel Cell (SOFC) electrode, this paper presents a novel segmentation method based on Gaussian Mixture Model (GMM). A coarseness-entropy adaptive factor is defined to incorporate the spatial information based on Markov Random Filed (MRF) into GMM. Furthermore the proposed method defines can control the trade-off between robustness to noise and effectiveness of preserving the details. Experimental results show that the proposed method outperforms the compared method on three-phase microstructure identification.