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

Fu, Xiaowei; Yang, Xuefei; Guo, Chengzhen; Li, Xi

doi:10.1007/s11042-020-08753-5

Cited by 1 publication

(1 citation statement)

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“…In their approach, the authors introduced the two-stage LPG-PCA filtering (de-noising) framework to remove artifacts and equalize the brightness of images, as well as Fuzzy Clustering for phase segmentation. Another approach was made by Yang et al [28], who aided their Fuzzy C-means clustering algorithm with Particle Swarm Optimization in order to improve the adaptive search for initial clustering centers and help avoid local optima.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence-Based Methodology for Scanning Electron Microscope Image Segmentation of Solid Oxide Fuel Cell Anode

et al. 2021

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Segmentation of images from scanning electron microscope, especially multiphase, poses a drawback in their microstructure quantification process. The labeling process must be automatized due to the time consumption and irreproducibility of the manual labeling procedure. Here we show a swarm intelligence-driven filtration methodology performed on raw solid oxide fuel cell anode’s material images to improve the segmentation methods’ performance. The methodology focused on two significant parts of the segmentation process, which are filtering and labeling. During the first one, the images underwent filtering by applying a series of filters, whose operation parameters were determined using Particle Swarm Optimization upon a dedicated cost function. Next, Seeded Region Growing, k-Means Clustering, Multithresholding, and Simple Linear Iterative Clustering Superpixel algorithms were utilized to label the filtered images’ regions into consecutive phases in the microstructure. The improvement was presented for three different metrics: the Misclassification Ratio, Structural Similarity Index Measure, and Mean Squared Error. The obtained distribution of metrics’ performances was based on 200 images, with and without filtering. Results indicate an improvement up to 29%, depending on the metric and method used. The presented work contributes to the ongoing efforts to automatize segmentation processes fully for an increasing number of tomographic measurements, particularly in solid oxide fuel cell research.

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Section: Introductionmentioning

confidence: 99%