Automatic Lane Detection in Chromatography Images

  • Bruno M. Moreira
  • António V. Sousa
  • Ana M. Mendonça
  • Aurélio Campilho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


This paper proposes a method for automating the detection of lanes in Thin-Layer Chromatography images. Our approach includes a preprocessing step to detect the image region of interest, followed by background estimation and removal. This image is then projected onto the horizontal direction to integrate the information into a one-dimensional profile. A smoothing filter is applied to this profile and the outcome is the input of the lane detection process, which is performed in three phases. The first one aims at obtaining an initial set of candidate lanes that are further validated or removed in the second phase. The last phase is a refinement step that allows the inclusion of lanes that are not clearly distinguishable in the profile and that were not included in the initial set. The method was evaluated in 66 chromatography images and achieved values of recall, precision and F β -measure of 97.0%, 99.4% and 98.2%, respectively.


Biomedical image processing Image analysis Computer aided diagnosis Chromatography images 


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  1. 1.
    Fried, B., Sherma, J.: Thin-Layer Chromatography. Marcel Dekker, New York (1999)CrossRefGoogle Scholar
  2. 2.
    Rodrigues, L.G., Ferraz, M.J., Rodrigues, D., Pais-Vieira, M., Lima, D., Brady, R.O., Sá-Miranda, M.C.: Neurophysiological behavioral and morphological abnormalities in the Fabry knockout mice. Neurobiology of Disease 33(1), 48–56 (2009)CrossRefGoogle Scholar
  3. 3.
    Elstein, D., Altarescu, G., Beck, M.: Fabry Disease. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Houck, M.M., Siegel, J.A.: Fundamentals of Forensic Science. Elsevier, Oxford (2010)Google Scholar
  5. 5.
    Machado, A.M.C., Campos, M.F.M., Siqueira, A.M., de Carvalho, O.S.F.: An iterative algorithm for segmenting lanes in gel electrophoresis images. In: Proc. X Brazilian Symposium on Computer Graphics and Image Processing, pp. 140–146 (October 1997)Google Scholar
  6. 6.
    Elder, J.K., Southern, E.M.: Computer-aided analysis of one dimensional restriction fragment gels. In: Bishop, M.J., Rawlings, C.J. (eds.) Nucleic Acid and Protein Sequence Analysis - A Practical Aproach, pp. 165–172. IRL Press, Oxford (1987)Google Scholar
  7. 7.
    Bajla, I., Holländer, I., Fluch, S., Burg, K., Kollár, M.: An alternative method for electrophoretic gel image analysis in the GelMaster software. Computer Methods and Programs in Biomedicine 77, 209–231 (2005)CrossRefGoogle Scholar
  8. 8.
    Lin, C., Ching, Y., Yang, Y.: An Automatic Method to Compare the Lanes in Gel Electrophoresis (GE) Images. IEEE Transaction on Information Technology in Biomedicine 11(2), 179–189 (2007)CrossRefGoogle Scholar
  9. 9.
    Akbari, A., Fritz, A., Jackobsen, K.S.: Automatic lane detection and separation in one dimensional gel images using continuous wavelet transform. The Royal Society of Chemistry, Analytical Methods 2, 1360–1371 (2010)Google Scholar
  10. 10.
    Sousa, A.V., Aguiar, R.L., Mendonça, A.M., Campilho, A.C.: Automatic Lane and Band Detection in Images of Thin Layer Chromatography. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 158–165. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Sotaquira, M.: On the use of distance maps in the analysis of 1D DNA gel images. In: ICDIP 2009: Proceedings of the International Conference on Digital Image Processing, pp. 172–176. IEEE Computer Society (2009)Google Scholar
  12. 12.
    Barrantes, P., Alvarado, P.: Lane Detection on Gel Electrophoresis Images using Active Shape Models. In: Proc. of the Conference on Technologies for Sustainable Development, TSD 2011, pp. 43–46 (February 2011)Google Scholar
  13. 13.
    Mendonça, A.M., Sousa, A.V., Sá-Miranda, M.C., Campilho, A.C.: Automatic segmentation of chromatographic images for region of interest delineation. In: Proc. SPIE, vol. 7962, pp. 79623B1-79623B7 (2011)Google Scholar
  14. 14.
    Chau, F., Liang, Y., Gao, J., Shao, X.: Chemometrics – From Basics to Wavelet Transform. John Wiley & Sons, New Jersey (2004)Google Scholar
  15. 15.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing. Syndicate of the University of Cambridge (1992)Google Scholar
  16. 16.
    Soille, P., Jones, C.D., Smith, A.B., Roberts, E.F.: Morphological Image Analysis – Principles and Applications. Springer (2004)Google Scholar
  17. 17.
    Visa, S., Ralescu, A.: Learning Imbalanced and Overlapping Classes using Fuzzy Sets. In: Workshop on Learning from Imbalanced Datasets II, ICML (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bruno M. Moreira
    • 1
    • 2
  • António V. Sousa
    • 1
    • 3
  • Ana M. Mendonça
    • 1
    • 2
  • Aurélio Campilho
    • 1
    • 2
  1. 1.Instituto de Engenharia BiomédicaUniversidade do PortoPortoPortugal
  2. 2.Instituto Superior de Engenharia do PortoInstituto Politécnico do PortoPortoPortugal
  3. 3.Faculdade de EngenhariaUniversidade do PortoPortoPortugal

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