Noise suppressed and bias field corrected image segmentation method for porous Ni-YSZ anode microstructure

Accurate three-phase identification from Solid Oxide Fuel Cell (SOFC) anode micrograph is challenged by both noise and intensity inhomogeneity. In this paper, a novel framework is proposed for porous Ni-YSZ cermet anode Optical Microscopy (OM) image segmentation. The proposed framework takes advantage of a statistical model in which an observed image is decomposed into two multiplicative components (bias field and true image) and one additive component (noise). A two-stage Principal Component Analysis with Local Pixel Grouping (LPG-PCA) denoising algorithm is firstly performed to suppress additive noise, it can preserve more image local structural features by modeling a pixel and its nearest neighbors as a vector variable and selecting training samples with similar contents to this variable in a local window for PCA transformation. In order to enhance the robustness to noise, uneven illumination and other outliers, a kernel metric is introduced into fuzzy clustering method embedded with bias field correction for image segmentation. The proposed method has been compared to other state-of-the-art segmentation algorithms on both simulated images and real SOFC anode OM images. Extensive experiments results have demonstrated that the proposed framework can successfully eliminate the influence of both uneven illumination and noise on real Ni/YSZ anode OM images to obtain a better three-phase identification accuracy. The high-quality segmentation results lay firm foundation for accurate microstructural parameter extraction.