Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-20T00:15:56.883Z Has data issue: false hasContentIssue false
  • English
  • Français

New Image Texture Analysis, and Application to Polymer Membrane Surface Morphologies and Roughness

Published online by Cambridge University Press:  20 September 2018

Clifford S. Todd Open the ORCID record for Clifford S. Todd [Opens in a new window]  and
William A. Heeschen
Clifford S. Todd*
Affiliation:
Analytical Science, Core R&D, The Dow Chemical Company, 1897 Building, Midland, MI 48667, USA
William A. Heeschen
Affiliation:
Analytical Science, Core R&D, The Dow Chemical Company, 1897 Building, Midland, MI 48667, USA
*
Author for correspondence: Clifford S. Todd, E-mail: CTodd2@dow.com
Get access

Abstract

A new method of image texture analysis is presented, based on the mean and standard deviation of gray levels within domains in an image. The calculations are performed recursively on domains of various sizes within the images. These gray level calculations are used as the input matrix for principal component analysis. The technique analyzes the entire image as a whole and is not for image segmentation. The analysis routine operates across all distances, frequencies and directions in the image, and is not computationally burdensome. The method was applied to scanning electron microscope images of reverse osmosis membranes on domains from 23 nm to 3 µm. The texture analysis technique performed well in identifying the surface morphology and, once calibrated, in predicting the surface roughness as measured by atomic force microscopy.

Keywords

texturemembranesmorphologyprincipal componentSEM
Type
Materials Science Applications
Information
Microscopy and Microanalysis , Volume 24 , Issue 5 , October 2018 , pp. 471 - 477
Copyright
© Microscopy Society of America 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Azari, K, Bogoya-Forero, W, Duchesne, C Tessier, J (2017) Measurement of vibrated bulk density of coke particle blends using image texture analysis. J Microsc 69, 1613–1623.Google Scholar
Beebe, KR, Pell, RJ Seasholtz, MB (1998) Chemometrics: A Practical Guide. New York, NY: John Wiley & Sons.Google Scholar
Bharati, MH, Liu, JJ MacGregor, JF (2004) Image texture analysis: Methods and comparisons. Chemo Intell Lab Sys 72, 57–71.Google Scholar
Duchesne, C, Liu, JJ MacGregor, JF (2012) Multivariate image analysis in the process industries: A review. Chemo Intell Lab Sys 117, 116–128.Google Scholar
Facco, P, Masiero, A, Bezzo, F, Barolo, M Beghi, A (2011) Improved multivariate image analysis for product quality monitoring. Chemo Intell Lab Sys 109, 42–50.Google Scholar
Fang, Y Duranceau, SJ (2013) Study of the effect of nanoparticles and surface morphology on reverse osmosis and nanofiltration membrane productivity. Membranes 3, 196–225.Google Scholar
Goldstein, JI, Newbury, DE, Joy, DC, Lyman, CE, Echlin, P, Lifshin, E, Sawyer, L Michael, JR (2003) Scanning Electron Microscopy and X-Ray Microanalysis, 3rd ed New York, NY: Springer.Google Scholar
Grossmann, A Morlet, J (1984) Decomposition of Hardy functions into square integrable wavelets of constant shape. Soc Int Am Math J Math Anal 15, 723–736.Google Scholar
Haar, A (1910) Zur Theorie der orthogonalen Funktionensysteme (Erste Mitteilung.). Math Annal 69, 331–371.Google Scholar
Haralick, RM, Shanmugam, K Dinstein, I (1973) Textural features for image classification. IEEE Trans SMC 3, 610–621.Google Scholar
Hirose, M, Ito, H Kamiyama, Y (1996) Effect of skin layer surface structures on the flux behaviour of RO membranes. J Membr Sci 121, 209–215.Google Scholar
Hobbs, C, Hong, S Taylor, J (2006) Effect of surface roughness on fouling of RO and NF membranes during filtration of a high organic surficial groundwater. J Water Supply 55, 559–570.Google Scholar
Hoek, EMV, Bhattacharjee, S Elimelech, M (2003) Effect of membrane surface roughness on colloid-membrane DLVO interactions. Langmuir 19, 4836–4847.Google Scholar
Jolliffe, IT (2002) Principal Component Analysis, 2nd ed. New York, NY: Springer.Google Scholar
Katz, A Thompson, AH (1985) Fractal sandstone pores: Implications for conductivity and pore formation. Phys Rev Lett 54(12), 1325.Google Scholar
Kelsey, KC Neff, JC (2014) Estimates of aboveground biomass from texture analysis of Landsat imagery. Rem Sens 6, 6407–6422.Google Scholar
Kirk, TB, Stachowiak, GW Batchelor, AW (1991) Fractal parameters and computer image analysis applied to wear particles isolated by ferrography. Wear 145(2), 347–365.Google Scholar
Kwak, S-Y, Jung, SG Kim, SH (2001) Structure–motion–performance relationship of flux-enhanced reverse osmosis (RO) membranes composed of aromatic polyamide thin films. Environ Sci Tech 35, 4334–4340.Google Scholar
Mandelbrot, BB (1982) The Fractal Geometry of Nature. San Francisco, CA: WH Freeman.Google Scholar
Moraru, L, Moldovanu, S, Culea-Florescu, A-L, Bibicu, D, Ashour, AS Dey, N (2017) Texture analysis of parasitological liver fibrosis images. Microsc Res Tech 80, 862–869.Google Scholar
Pantic, I, Dacic, S, Brkic, P, Lavrnja, I, Pantic, S, Jovanovic, T Pekovic, S (2014) Application of fractal and grey level co-occurrence matrix analysis in evaluation of brain corpus callosum and cingulum architecture. Microsc Microanal 20, 1373–1381.Google Scholar
Pantic, I, Pantic, S Basta-Jovanovic, G (2012) Gray level co-occurrence matrix texture analysis of germinal center light zone lymphocyte nuclei: Physiology viewpoint with focus on apoptosis. Microsc Microanal 18, 470–475.Google Scholar
Petrosian, AA Meyer, FG (2013) Wavelets in Signal and Image Analysis: From Theory to Practice. Dordrecht, The Netherlands: Kluwer Academic Publishers.Google Scholar
Risović, D, Poljaček, SM, Furić, K Gojo, M (2008) Inferring fractal dimension of rough/porous surfaces—A comparison of SEM image analysis and electrochemical impedance spectroscopy methods. Appl Surf Sci 255(5), 3063–3070.Google Scholar
Russ, JC (1993) Fractal Surfaces. New York, NY: Plenum Press.Google Scholar
Russ, JC (1998) Fractal dimension measurement of engineering surfaces. Int J Mach Tools Manuf 38(5–6), 567–571.Google Scholar
Todd, CS Heeschen, WA (2016) Image texture analysis and application to acicular mullite porous ceramic microstructure. Microsc Microanal 22(Suppl 3), 544–545.Google Scholar
Vrijenhoek, EM, Hong, S Elimelech, M (2001) Influence of membrane surface properties on initial rate of colloidal fouling of reverse osmosis and nanofiltration membranes. J Membr Sci 188, 115–128.Google Scholar
Vujasinovic, T, Pribic, J, Kanjer, K, Milosevic, NT, Tomasevic, Z, Milovanovic, Z, Nikolic-Vukosavljevic, D Radulovic, M (2015) Gray-level co-occurrence matrix texture analysis of breast tumor images in prognosis of distant metastasis risk. Microsc Microanal 21(3), 646–654.Google Scholar
Xia, D-H, Ma, C, Song, S, Jin, W, Behnamian, Y, Fan, H, Wang, J, Gao, Z Hu, W (2017) Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov–Sinai entropy. Corros Sci 120, 251–256.Google Scholar
Yin, Z, Yang, C, Long, C Li, A (2017) Influence of surface properties of RO membrane on membrane fouling for treating textile secondary effluent. Environ Sci Pollut Res Int 24(19), 16253–16262.Google Scholar
Youssef, D, El-Ghandoor, H, Kandel, H, El-Azab, J Hassab-Elnaby, S (2017) Estimation of articular cartilage surface roughness using gray-level co-occurrence matrix of laser speckle image. Materials 10, 714.Google Scholar
Zakeri, H, Yamazaki, F Liu, W (2017) Texture analysis and land cover classification of Tehran using polarimetric synthetic aperture radar imagery. Appl Sci 7(5), 452.Google Scholar
Supplementary material: File

Todd and Heeschen supplementary material

Todd and Heeschen supplementary material 1

Download Todd and Heeschen supplementary material(File)
File 13.8 MB