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

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Abstract

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.

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References

  1. Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199

    Article  Google Scholar 

  2. Bezdek JC (1981) Objective function clustering. In pattern recognition with fuzzy objective function algorithms (pp 43–93). Springer, Boston, MA

  3. Chen S, Zhang D (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(4):1907–1916

    Article  Google Scholar 

  4. Fu X, Wang Y, Chen L, Tian J (2015) An image despeckling approach using quantum-inspired statistics in dual-tree complex wavelet domain. Biomedical Signal Processing and Control 18:30–35

    Article  Google Scholar 

  5. Fu X, Xiang Y, Chen L, Xu X, Li X (2015) Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering. J Power Sources 300:57–68

    Article  Google Scholar 

  6. Fu X, Xiang Y, Chen L, Xu X, Li X (2016) A novel Ni/YSZ anode image segmentation method for solid oxide fuel cell electrodes microstructure. Fuel Cells 16(6):810–821

    Article  Google Scholar 

  7. Wu X, Xu Y, Xue T, Zhao D, Jiang J, Deng Z, Fu X, Li X (2019) Health state prediction and analysis of SOFC system based on the datadriven entire stage experiment. Appl Energy 248:126–140.

  8. Gong M, Liang Y, Shi J, Ma W, Ma J (2013) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584

    Article  MathSciNet  MATH  Google Scholar 

  9. Guan Y, Li W, Gong Y, Liu G, Zhang X, Chen J, Wang H (2011) Analysis of the three-dimensional microstructure of a solid-oxide fuel cell anode using nano X-ray tomography. J Power Sources 196(4):1915–1919

    Article  Google Scholar 

  10. Holzer L, Münch B, Iwanschitz B, Cantoni M, Hocker T, Graule T (2011) Quantitative relationships between composition, particle size, triple phase boundary length and surface area in nickel-cermet anodes for solid oxide fuel cells. J Power Sources 196(17):7076–7089

    Article  Google Scholar 

  11. Iwai H, Shikazono N, Matsui T, Teshima H, Kishimoto M, Kishida R, Muroyama H (2010) Quantification of SOFC anode microstructure based on dual beam FIB-SEM technique. J Power Sources 195(4):955–961

    Article  Google Scholar 

  12. Krinidis S, Chatzis V (2010) A robust fuzzy local information C-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337

    Article  MathSciNet  MATH  Google Scholar 

  13. Lanzini A, Leone P, Asinari P (2009) Microstructural characterization of solid oxide fuel cell electrodes by image analysis technique. J Power Sources 194(1):408–422

    Article  Google Scholar 

  14. Lee KR, Choi SH, Kim J, Lee HW, Lee JH (2005) Viable image analyzing method to characterize the microstructure and the properties of the Ni/YSZ cermet anode of SOFC. J Power Sources 140(2):226–234

    Article  Google Scholar 

  15. Lee JH, Heo JW, Lee DS, Kim J, Kim GH, Lee HW, … Moon JH (2003) The impact of anode microstructure on the power generating characteristics of SOFC. Solid State Ionics 158(3–4):225–232

    Article  Google Scholar 

  16. Lee DS, Lee JH, Kim J, Lee HW, Song HS (2004) Tuning of the microstructure and electrical properties of SOFC anode via compaction pressure control during forming. Solid State Ionics 166(1–2):13–17

    Article  Google Scholar 

  17. Lee JH, Moon H, Lee HW, Kim J, Kim JD, Yoon KH (2002) Quantitative analysis of microstructure and its related electrical property of SOFC anode, Ni–YSZ cermet. Solid State Ionics 148(1–2):15–26

    Article  Google Scholar 

  18. Li C, Gore JC, Davatzikos C (2014) Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn Reson Imaging 32(7):913–923

    Article  Google Scholar 

  19. Lim JS (1990) Two-dimensional signal and image processing. Englewood Cliffs, NJ, Prentice Hall, 1990, 710 p

  20. Luo J, Yan D, Fang D, Liang F, Pu J, Chi B, Li J (2013) Electrochemical performance and thermal cyclicability of industrial-sized anode supported planar solid oxide fuel cells. J Power Sources 224:37–41

    Article  Google Scholar 

  21. McLachlan GJ, Lee SX, Rathnayake SI (2000) Finite mixture models. Ann Rev Stat Appl (0)

  22. Mogensen M, Skaarup S (1996) Kinetic and geometric aspects of solid oxide fuel cell electrodes. Solid State Ionics 86:1151–1160

    Article  Google Scholar 

  23. Nguyen TM, Wu QJ (2013) A fuzzy logic model based Markov random field for medical image segmentation. Evol Syst 4(3):171–181

    Article  Google Scholar 

  24. Nikou C, Galatsanos NP, Likas AC (2007) A class-adaptive spatially variant mixture model for image segmentation. IEEE Trans Image Process 16(4):1121–1130

    Article  MathSciNet  Google Scholar 

  25. Resini C, Delgado MCH, Presto S, Alemany LJ, Riani P, Marazza R, Busca G (2008) Yttria-stabilized zirconia (YSZ) supported Ni–co alloys (precursor of SOFC anodes) as catalysts for the steam reforming of ethanol. Int J Hydrog Energy 33(14):3728–3735

    Article  Google Scholar 

  26. Rouhparvar H, Panahi A (2015) A new definition for defuzzification of generalized fuzzy numbers and its application. Appl Soft Comput 30:577–584

    Article  Google Scholar 

  27. Sendur L, Selesnick IW (2002) Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans Signal Process 50(11):2744–2756

    Article  Google Scholar 

  28. Shearing PR, Bradley RS, Gelb J, Lee SN, Atkinson A, Withers PJ, Brandon NP (2011) Using synchrotron X-ray nano-CT to characterize SOFC electrode microstructures in three-dimensions at operating temperature. Electrochem Solid-State Lett 14(10):B117–B120

    Article  Google Scholar 

  29. Zhang L, Dong W, Zhang D, Shi G (2010) Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recogn 43(4):1531–1549

    Article  MATH  Google Scholar 

  30. Zhang L, Li X, Jiang J, Li S, Yang J, Li J (2015) Dynamic modeling and analysis of a 5-kW solid oxide fuel cell system from the perspectives of cooperative control of thermal safety and high efficiency. International Journal of Hydrogen Energy 40(1):456–476

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61873323), the open fund project of State key laboratory of Material Processing and Die & Mould Technology (No.P2018-016), Hubei Provincial Natural Science Foundation of China (No.2017CFB506, 2016CFA037), the Wuhan Science and Technology Plan Project (grant no. 2018010401011292), the WUST National Defence Pre-research Foundation (No. GF201912), the open fund project of Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (No. 2016znss02A, znxx2018ZD01).

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Xiaowei Fu, Xuefei Yang & Chengzhen Guo

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China

    Xiaowei Fu, Xuefei Yang & Chengzhen Guo

  3. State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, China

    Xiaowei Fu

  4. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Xi Li

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  1. Xiaowei Fu
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Correspondence to Xi Li.

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Fu, X., Yang, X., Guo, C. et al. Noise suppressed and bias field corrected image segmentation method for porous Ni-YSZ anode microstructure. Multimed Tools Appl 79, 18859–18881 (2020). https://doi.org/10.1007/s11042-020-08753-5

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  • DOI: https://doi.org/10.1007/s11042-020-08753-5

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