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

Maciej Chalusiak, Weronika Nawrot, Szymon Buchaniec and Grzegorz Brus
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Maciej Chalusiak: Department of Fundamental Research in Energy Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., 30059 Cracow, Poland
Weronika Nawrot: Department of Fundamental Research in Energy Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., 30059 Cracow, Poland
Szymon Buchaniec: Department of Fundamental Research in Energy Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., 30059 Cracow, Poland
Grzegorz Brus: Department of Fundamental Research in Energy Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., 30059 Cracow, Poland

Energies, 2021, vol. 14, issue 11, 1-17

Abstract: 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.

Keywords: solid oxide fuel cell; microstructure; anode; image filtering; segmentation; particle swarm optimization; electron tomography; image processing; FIB-SEM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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