An improved algorithm for classification of graphite grains in cast iron microstructure images using geometric shape features

  • Pattan Prakash
  • V. D. Mytri
  • P. S. Hiremath


Physical properties of a material depend on its microstructure characteristics. Carbon in the form of graphite is often used as an additive in the production of cast iron [3]. The microstructure of graphite within cast iron has major effects on the casting’s mechanical properties. When graphite arranges itself as thin fl akes, the result is gray iron, which is hard and brittle. When graphite takes the form of spherical nodules the result is nodular iron, which is soft and malleable.


Radial Basis Function Cast Iron Radial Basis Function Neural Network Nodular Cast Iron Shape Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A.K. Jain, “Fundamentals of Digital Image Processing”. Prentice-Hall, Englewood Cliffs, NJ, 1989Google Scholar
  2. 2.
    Chen Zhibin, Yu Yongquan, Chen eqing, Yang Shaomin, “Fuzzy Recognition of Graphite Morphology in Nodular Cast Iron Based on Evolution Strategy”, Proc. of the Fourth Intl. Conference on Machine Learning and Cybernetics, Guangzhou, Aug. 2005, pp 18-21Google Scholar
  3. 3.
    Handbook Committee, Handbook of ASM International, Vol 9, Metallography and Microstructures. ISBN:0-87170706-3Google Scholar
  4. 4.
    H. E. Henderson’s article titled “Ultrasonic Velocity Technique for Quality Assurance”, appearing in the Foundry Trade Journal,Feb. 21, 1974, at pages 203-208Google Scholar
  5. 5.
    Longin Jan Latecki and Rolf Lak¨amper and Ulrich Eckhardt, “Shape Descriptors for Nonrigid Shapes with a Single Closed Contour”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2000, pp 424-429Google Scholar
  6. 6.
    L.Wojnar, Image Analysis, Applications in Materials Engineering, CRC Press, 1999Google Scholar
  7. 7.
    Milan Sonka, Vaclav Hlavac, Rogen Boyle, “Image processing analysis and machine vision” 2e, Thomson Publishing Company, 2007Google Scholar
  8. 8.
    Otsu N., “A Threshold Selection Method from Gray-level Histograms”, IEEE Trans, on Systems, Man and Cybernetics, Vol. 9 No. 1 1979, pp. 62-66MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pattan Prakash, V.D. Mytri and P.S. Hiremath, “Classification of Graphite Grains with Simple Shape Descriptors”, Intl. Jl. on Engg. and Tech. (IJENGG), Vol. 4, No. 2, pp.37-42, 2009Google Scholar
  10. 10.
    Pattan Prakash, V.D. Mytri, P.S.Hiremath,, “Automatic Microstructure Image Analysis for Classification and Quantification of Phases of Material”, Proc. of International Conference on Systemics, Cybernetics and Informatics (ICSCI- 2009) Jan 17-10,2009, pp 308–311Google Scholar
  11. 11.
    Pattan Prakash, V.D. Mytri, P.S. Hiremath, “Classification of Cast Iron Based on Graphite Grain Morphology using Neural Network Approach”, Proc. of Intl. Conf. on Digital Image Processing, (ICDIP 2010), Feb 26-28, 2010 (communicated)Google Scholar
  12. 12.
    Practical Guide to Image Analysis, ASM International, 2000Google Scholar
  13. 13.
    University of Cambridge,Contributed Microstructures library: http://www.doitpoms. and starteng. Html
  14. 14.
    Wanda Benesova, Alfred Rinnhofer, Gerhard Jacob, “Determining The Average Grain Size of Super-Alloy Micrographs”, ICIP-2006, Joanneum Research Institute of Digital Image Processing, Graz, Austria, 2006, pp 2749-2752Google Scholar
  15. 15.
    Xaviour Arnould, Michel Coster, Jean-Louis Chermant, Liliane Chermant, Thierry Chartier and Abder Elmoataz, “Segmentation and Grain Size of Cera mics”, Image Anal Stereol, France 2001, pp.131-135Google Scholar
  16. 16.
    F. Mokhtarian, S. Abbasi, and J. Kittler. Efficient and robust retrieval by shape content through curvature scale space. In A. W. M. Smeulders and R. Jain, editors, Image Databases and Multi-Media Search, pages 51–58. World Scientific Publishing, Singapore, 1997Google Scholar
  17. 17.
    F. Mokhtarian and A. K. Mackworth. A theory of multiscale, curvature-based shape representation for planar curves. IEEE Trans. 4:789–805, 1992Google Scholar
  18. 18.
    G. Chuang and C.-C. Kuo. Wavelet descriptor of planar curves: Theory and applications. IEEE Trans. on Image Processing, 5:56–70, 1996CrossRefGoogle Scholar
  19. 19.
    Khotanzan and Y. H. Hong. Invariant image recognition by Zernike moments. IEEE Trans. PAMI, 12:489–497, 1990Google Scholar
  20. 20.
    Przemyslaw Lagodzinski and Bogdan Smolka On the Application of Distance Transformation in Digital Image Colorization, Computer Recognition Systems 2, ASC 45, pp. 108–116, 2007Google Scholar

Copyright information

© Springer India Pvt. Ltd 2011

Authors and Affiliations

  • Pattan Prakash
    • 1
  • V. D. Mytri
    • 2
  • P. S. Hiremath
    • 3
  1. 1.Department of Computer Science & Engg.PDA College of EngineeringGulbargaIndia
  2. 2.Computer Engineering DepartmentGND Engineering CollegeBidarIndia
  3. 3.Department of Computer ScienceGulbarga UniversityGulbargaIndia

Personalised recommendations