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Automatic determination of the size of elliptical nanoparticles from AFM images

  • Jiří Sedlář
  • Barbara Zitová
  • Jaromír Kopeček
  • Jan Flusser
  • Tatiana Todorciuc
  • Irena Kratochvílová
Research Paper

Abstract

The objective of this work was to develop an accurate method for automatic determination of the size of elliptical nanoparticles from atomic force microscopy (AFM) images that would yield results consistent with results of manual measurements by experts. The proposed method was applied on phenylpyridyldiketopyrrolopyrrole (PPDP), a granular organic material with a wide scale of application and highly sensitive particle-size properties. A PPDP layer consists of similarly sized elliptical particles (c. 100 nm × 50 nm) and its properties can be estimated from the average length and width of the particles. The developed method is based on segmentation of salient particles by the watershed transform and approximation of their shapes by ellipses computed by image moments; it estimates the lengths and widths of the particles by the major and minor axes, respectively, of the corresponding ellipses. Its results proved to be consistent with results of manual measurements by a trained expert. The comparison showed that the developed method could be used in practice for precise automatic measurement of PPDP particles in AFM images.

Keywords

Atomic force microscopy Image moments Pyrrole derivatives Size determination Watershed segmentation 

Notes

Acknowledgments

This work was partly supported by the Czech Science Foundation Grants No. GAP103/11/1552 and GAP304/10/1951, and by the Technological Agency of the Czech Republic Grant No. TA01011165.

References

  1. Binnig G, Quate CF, Gerber C (1986) Atomic force microscope. Phys Rev Lett 56(9):930–933. doi: 10.1103/PhysRevLett.56.930 CrossRefGoogle Scholar
  2. Cloppet F, Boucher A (2010) Segmentation of complex nucleus configurations in biological images. Pattern Recogn Lett 31(8):755–761. doi: 10.1016/j.patrec.2010.01.022 CrossRefGoogle Scholar
  3. Crum WR, Camara O, Hill DLG (2006) Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging 25(11):1451–1461. doi: 10.1109/TMI.2006.880587 CrossRefGoogle Scholar
  4. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302CrossRefGoogle Scholar
  5. Fekete L, Kůsová K, Petrák V, Kratochvílová I (2012) AFM topographies of densely packed nanoparticles: a quick way to determine the lateral size distribution by autocorrelation function analysis. J Nanoparticle Res 14(8):1–10. doi: 10.1007/s11051-012-1062-7 CrossRefGoogle Scholar
  6. Flusser J, Suk T, Zitová B (2009) Moments and moment invariants in pattern recognition. Wiley, ChichesterCrossRefGoogle Scholar
  7. Gangopadhyay R, Molla MR (2011) Polypyrrole–polyvinyl alcohol stable nanodispersion: a prospective conducting black ink. J Polym Sci B: Polym Phys 49(11):792–800. doi: 10.1002/polb.22216 CrossRefGoogle Scholar
  8. Mironov VL (2004) Fundamentals of scanning probe microscopy. Russian Academy of Sciences, Institute for Physics of Microstructures, Nizhniy Novgorod, URL http://physics.gu.se/popok/SPM/Mironov.pdf
  9. Mizuguchi J, Imoda T, Takahashi H, Yamamaki H (2006) Polymorph of 1,4-diketo-3,6-bis-(4′-dipyridyl)-pyrrolo-[3,4-c]pyrrole and their hydrogen bond network: A material for H2 gas sensor. Dyes Pigment 68(1):47–52. doi: 10.1016/j.dyepig.2005.01.001 CrossRefGoogle Scholar
  10. NTEGRA Prima (NT-MDT) URL http://www.ntmdt.com/modular-afm/prima
  11. NT-MDT (1998-2013) URL http://www.ntmdt.com/
  12. Qu S, Tian H (2012) Diketopyrrolopyrrole (DPP)-based materials for organic photovoltaics. Chem Commun 48(25):3039–3051. doi: 10.1039/C2CC17886A CrossRefGoogle Scholar
  13. Salyk O, Vyňuchal J, Kratochvílová I, Todorciuc T, Pavluch J, Toman P (2010) Study of phenylpyridyldiketopyrrolopyrrole interaction with hydrogen in gas and in acids. Dyes Pigment 207(10):2327–2333. doi: 10.1002/pssa.200925535 Google Scholar
  14. Song KT, Cho SH, Lee JY (2007) Conjugated polymers: theory, synthesis, properties, and characterization, 3rd edn. CRC Press, Boca Raton, chap Recent Advances in Polypyrrole, pp 8–1–8–87. Handbook of Conducting PolymersGoogle Scholar
  15. Toman M (2001) Analýza STM snímků pořízených elektronovým mikroskopem [Analysis of STM images acquired by electron microscope]. Master’s thesis, Charles University, PragueGoogle Scholar
  16. Villarrubia JS (1997) Algorithms for scanned probe microscope image simulation, surface reconstruction, and tip estimation. J Res Natl Inst Stand Technol 102:102–425CrossRefGoogle Scholar
  17. Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598. doi: 10.1109/34.87344 CrossRefGoogle Scholar
  18. Vyňuchal J, Luňák S Jr, Hatlapatková A, Hrdina R, Lyčka A, Havel L, Vyňuchalová K, Jirásko R (2008) The synthesis, absorption, fluorescence and photoisomerisation of 2-aryl-4-arylmethylidene-pyrroline-5-ones. Dyes Pigment 77(2):266–276. doi: 10.1016/j.dyepig.2007.05.001 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jiří Sedlář
    • 1
  • Barbara Zitová
    • 1
  • Jaromír Kopeček
    • 2
  • Jan Flusser
    • 1
  • Tatiana Todorciuc
    • 3
  • Irena Kratochvílová
    • 2
  1. 1.Institute of Information Theory and Automation, Academy of Sciences of the Czech RepublicPrague 8Czech Republic
  2. 2.Institute of Physics, Academy of Sciences of the Czech RepublicPrague 8Czech Republic
  3. 3.Faculty of Chemical Engineering and Environmental ProtectionGheorghe Asachi Technical UniversityIasiRomania

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