Automatic Extraction of DNA Profiles in Polyacrilamide Gel Electrophoresis Images

  • Francisco Silva-Mata
  • Isneri Talavera-Bustamante
  • Ricardo González-Gazapo
  • Noslén Hernández-González
  • Juan R. Palau-Infante
  • Marta Santiesteban-Vidal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper is presented a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrilamide gel electrophoresis based on image processing. A software which implements this method was developed, composed by four modules: Digital image acquisition, image preprocessing, feature extraction and classification, and DNA profile extraction. The use of different types of algorithms as: C4.5 Decision Trees, Support Vector Machines and Leader Algorithm are needed to resolve all the tasks. The experimental results show that this method has a very nice computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.


Support Vector Machine Automatic Extraction Profile Extraction Sobel Edge Detector Codebook Vector 
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.


  1. 1.
    Gill, P., Urquhart, A., Millican, E., Oldroyd, N., Watson, S.: Criminal intelligence Databases and interpretation of STRs. Advances in Forensic Haemogenetics 6, 235–242 (1996)Google Scholar
  2. 2.
    Lander, E.S.: DNA fingerprinting: The NRC report, Science, vol.260, pp. 1221 (1993)Google Scholar
  3. 3.
    Lewontin, R.C., Hartl, D.L.: Population genetics in forensic DNA typing. Science 254, 1745–1750 (1991)CrossRefGoogle Scholar
  4. 4.
    Weber, J., May, P.: Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. Am. J. Hum. Genet. 44, 388–396 (1989)Google Scholar
  5. 5.
    Estrada, C.: Techniques for DNA analysis in forensic genetics (2001),
  6. 6.
    Shortley, G., Dudly, W.: Elements of Physics. B.E.E. In: Illumination and Photometry, ch. 24, 3rd edn., p. 506 (1966)Google Scholar
  7. 7.
    Kacmazmarek, B., Walczak, B., Jong, S., Vandeginste, B.G.M.: Preprocessing of 2-D gel electrophoresis images. Analytical Chemistry 75, 3631–3636 (2003)CrossRefGoogle Scholar
  8. 8.
    Kacmazmarek, B., Walczak, B., Jong, S., Vandeginste, B.G.M.: Enhancement of images from 2-D gel electrophoresis. In: Proceedings 9th International Conference, CAC 2004, p. 171 (2004)Google Scholar
  9. 9.
    Stockham, T.G.: Image processing in the context of a Visual Model. Proc, IEEE 60(7), 828–842 (1972)CrossRefGoogle Scholar
  10. 10.
    Short, J., Kittler, J., Messer, K.: A comparison of photometric normalization algorithms for face verification. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, FGR 2004 (2004)Google Scholar
  11. 11.
    Gonzalez, R., Woods, R.: Digital Image Processing using MATLAB, 2nd edn., pp. 385–387. Prentice Hall, Englewood Cliffs (2004)Google Scholar
  12. 12.
    Quinlan, R.J.: C4.5: Programs for Machine Learnig (Morgan Kaufmann Series in Machine Learning). Paperback- January 15 (1993)Google Scholar
  13. 13.
    Vapnik, V., Chervonenkis, A.: Theory of Pattern Recognition. Nauka, Moscow (1974)Google Scholar
  14. 14.
    Vapnik, V.: The nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  15. 15.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  16. 16.
    Cristianini, Shawe-Taylor, J.: An introduction to Support Vector Machine. Cambridge University Press, Cambridge (2000)Google Scholar
  17. 17.
    Scholkopf, C., Burges, J., Smola, A.: Advances in Kernel methods: Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Xu, Z., Buckles, B.: DNA Sequence Classification by using Support Vector Machine. EECS, Tulane UniversityGoogle Scholar
  19. 19.
    Hartigan, J.: Clustering Algorithm. John Wiley and Sons, New York (1975)Google Scholar
  20. 20.
    Alvarez, A., Ruiz, J., Sanchiz, M.: Typical Segment Descriptors: A new method for shape description and classification. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 512–520. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Ching-Huei, T.: A.NET Implementation of Support Vector Machine.IESL MITVersion 0.8b, October 25 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Francisco Silva-Mata
    • 1
  • Isneri Talavera-Bustamante
    • 1
  • Ricardo González-Gazapo
    • 1
  • Noslén Hernández-González
    • 1
  • Juan R. Palau-Infante
    • 1
  • Marta Santiesteban-Vidal
    • 2
  1. 1.Advanced Technologies Applications CenterMINBASCuba
  2. 2.Central Criminologist LaboratoryCuba

Personalised recommendations