Skip to main content
Log in

Automatic analysis of immunocytochemically stained tissue samples

  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

An automatic colour image segmentation and cell counting software system has been developed for immunocytochemical analysis of stained tissue samples. The system was designed to count the total number of positive and negative cells in tissue samples treated with cytokine DNA probes from pigs naturally parasitised with Taenia solium metacestodes, using in situ hybridisation. A reaction index was calculated as the ratio of the number of cells with a positive reaction to the total number of cells (positives plus negatives) for each of five different probes. The objectives of automatic counting were to improve the reproducibility of the analysis and reduce the processing time of large image batches. A fast KNN classifier was used for colour segmentation. Watershed segmentation combined with edge detection was used to isolate individual cells that were then automatically labelled, using the results of the corresponding colour segmented image. Validation was performed on 122 non-training digital images with a total of 1069 positive cells and 1459 negative cells, with the following results: a mean true positive rate of 90.2% for positive cells and a mean true positive rate of 85.4% for negative cells. The corresponding mean false positive rates were 9.6% and 6.6%. The mean reaction index error of the automatic analysis was 5.35%. The processing of each digital image took 10 s on a Pentium IV PC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Baumgart, E., Schad, A., Vôlkl, A., andDariush Fahimi, H. (1997): ‘Detection of mRNAs encoding peroxisomal proteins by non-radiactive in situ hybridisation with digoxigenin-labelled cRNAs’,Histochem. Cell Biol.,108, pp. 371–379

    Google Scholar 

  • Bishop, C. M. (1997): ‘Neural networks for pattern recognition’ (Oxford University Press, 1997)

  • Castleman, K. R., Riopka, T. P., andQuiang, W. (1996): ‘FISH Image analysis’IEEE Eng. Med. Biol., pp. 67–75

  • Duda, R., andHart, P. (1973): ‘Pattern classification and scene analysis’, (Wiley, 1973)

  • Malpica, N., Ortiz de Solózano, C., Vaquero, J. J., Santos, A., Vallcorba, I., Garcia-Sagredo, M., andDel Pozo, F. (1997) ‘Applying watershed algorithms to the segmentation of clustered nuclei’,Cytometry,28, pp. 289–297

    Article  Google Scholar 

  • Marr, D. (1982).Vision (Freeman, San Francisco, 1982)

    Google Scholar 

  • Ranefall, P., Egevad, L., Nordin, B., andBengtsson, E. (1997): ‘A new method for segmentation of colour images applied to immunohistochemically stained cell nuclei’.Anal. Cell. Pathol. 15, pp. 145–156

    Google Scholar 

  • Ranefall, P., Wester, K., Andersson, A. C., Busch, C., andBengtsson, E. (1998): ‘Automatic quantification of immunohistochemically stained cell nuclei based on standard reference cells’,Anal. Cell. Pathol.,17, pp. 111–123.

    Google Scholar 

  • Russ, J. C. andRuss, J. C. (1988): ‘Improved implementation of a convex segmentation algorithm’,Acta Stereologica,7, pp. 33–40

    Google Scholar 

  • Russ, J. C. (2002): ‘The image processing handbook’. Fourth edn (CRC Press, 2002), pp. 425–434

  • Tato, P., White, A. C., Williams, K., Rodriguez, D., Solano, S., Sepúlveda, J., andMolinari, J. L. (1996): ‘Immunosupression and inhibition of inflammation in mice induced by a smallTaenia solium RNA-peptide to implantedT. solium metacestodes’,Parasitol. Res. 82, pp. 590–597

    Article  Google Scholar 

  • Vincent, L., andSoille, P. (1991): ‘Watersheds in digital spaces: An efficient algorithm based on immersion simulation’,IEEE Trans. Pattern Anal. Mach. Intell.,13, pp. 583–598

    Article  Google Scholar 

  • Willemse, F., Nap, M., Henzen-Logmans, S. C., andEggink, H. F. (1994): ‘Quantification of area percentage of immunohistochemical staining by true color image analysis with application of fixed thresholds’,Anal. Quant. Cytol. Histol.,16, pp. 357–364

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. Arámbula Cosío.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Arámbula Cosío, F., Márquez Flores, J.A., Padilla Castañeda, M.A. et al. Automatic analysis of immunocytochemically stained tissue samples. Med. Biol. Eng. Comput. 43, 672–677 (2005). https://doi.org/10.1007/BF02351042

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02351042

Keywords

Navigation