Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images

  • Lidia Sánchez
  • Nicolai Petkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5400)


In this work we review three methods proposed to estimate the fraction of alive sperm cells in boar semen samples. Images of semen samples are acquired, preprocessed and segmented in order to obtain images of single sperm heads. A model of intracellular density distribution characteristic of alive cells is computed by averaging a set of images of cells assumed to be alive by veterinarian experts. We quantify the deviation of the distribution of a cell from this model and use it for classification deploying three different approaches. One is based on a decision criterion used for single cell classification and gives misclassification error of 20.40%. The other two methods are focused on estimating the fraction of alive sperm in a sample, instead of single cell classification. One of them applies the least squares method, achieving an absolute error below 25% for 89% of the considered sample images. The other uses an iterative procedure to find an optimal decision criterion that equalizes the number of misclassifications of alive and dead cells. It provides an estimation of the fraction of alive cells that is within 8% of its actual value for 95% of the samples.


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  1. 1.
    Alegre, E., Fernández, R.A., Sánchez, L., Rodríguez, V., González, R., Tejerina, F., Domínguez, J.C.: Digital image segmentation methods for automatic quality evaluation of boar semen. Reproduction in Domestic Animals 40, 392 (2005)Google Scholar
  2. 2.
    Biehl, M., Pasma, P., Pijl, M., Sánchez, L., Petkov, N.: Classification of boar sperm head images using Learning Vector Quantization. In: European Symposium on Artificial Neural Networks (ESANN), pp. 545–550. D-side, Evere (2006)Google Scholar
  3. 3.
    García-Herreros, M., Aparicio, I.M., Barón, F.J., García-Marín, L.J., Gil, M.C.: Standardization of sample preparation, staining and sampling methods for automated sperm head morphometry analysis of boar spermatozoa. International Journal of Andrology 29, 553–563 (2006)CrossRefPubMedGoogle Scholar
  4. 4.
    Garrett, C., Gordon Baker, H.W.: A new fully automated system for the morphometric analysis of human sperm heads. Fertility and Sterility 63(6), 1306–1317 (1995)CrossRefPubMedGoogle Scholar
  5. 5.
    Hirai, M., Boersma, A., Hoeflich, A., Wolf, E., Föll, J., Aumüller, T.R., Braun, J.: Objectively measured sperm motility and sperm head morphometry in boars (Sus scrofa): relation to fertility and seminal plasma growth factors. Journal of Andrology 22, 104–110 (2001)PubMedGoogle Scholar
  6. 6.
    Jackson, J.E.: A User’s Guide to Principal Components. John Wiley and Sons, Inc., Chichester (1991)CrossRefGoogle Scholar
  7. 7.
    Linneberg, C., Salamon, P., Svarer, C., Hansen, L.K.: Towards semen quality assessment using neural networks. In: Neural Networks for Signal Processing IV. Proceedings of the 1994 IEEE Workshop, pp. 509–517 (1994)Google Scholar
  8. 8.
    Alaiz, R., González, M., Alegre, E., Sánchez, L.: Acrosome integrity classification of boar spermatozoon images using dwt and texture techniques. In: International Conference VipIMAGE - I ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. Taylor and Francis, Abington (2007)Google Scholar
  9. 9.
    Núñez-Martínez, I., Moran, J.M., Peña, F.J.: A three-step statistical procedure to identify sperm kinematic subpopulations in canine ejaculates: Changes after cryopreservation. Reproduction in Domestic Animals 41, 408–415 (2006)CrossRefPubMedGoogle Scholar
  10. 10.
    Oliva-Hernández, J., Corcuera, B.D., Pérez-Gutiérrez, J.F.: Epidermal Growth Factor (EGF) effects on boar sperm capacitation. Reproduction in Domestic Animals 40, 363 (2005)Google Scholar
  11. 11.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  12. 12.
    Petkov, N., Alegre, E., Biehl, M., Sánchez, L.: LVQ acrosome integrity assessment of boar sperm cells. In: Tavares, J.M.R.S., Natal Jorge, R.M. (eds.) Computational Modelling of Objects Represented in Images - Fundamentals, Methods and Applications; Proc. Int. Symp. CompImage 2006, Coimbra, Portugal, pp. 337–342. Taylor and Francis Group, London (2007)Google Scholar
  13. 13.
    Pinart, E., Bussalleu, E., Yeste, M., Briz, M., Sancho, S., Garcia-Gil, N., Badia, E., Bassols, J., Pruneda, A., Casas, I., Bonet, S.: Assessment of the functional status of boar spermatozoa by multiple staining with fluorochromes. Reproduction in Domestic Animals 40, 356 (2005)Google Scholar
  14. 14.
    Quintero-Moreno, A., Miró, J., Rigau, T., Rodríguez-Gil, J.E.: Regression analyses and motile sperm subpopulation structure study as improving tools in boar semen quality analysis. Theriogenology 61(4), 673–690 (2004)CrossRefPubMedGoogle Scholar
  15. 15.
    Rozeboom, K.J.: Evaluating boar semen quality. In: Animal Science Facts (2000)Google Scholar
  16. 16.
    Castejón, M., Suarez, S., Alegre, E., Sánchez, L.: Use of statistic texture descriptors to classify boar sperm images applying discriminant analysis. In: International Conference VipIMAGE - I ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. Taylor and Francis, Abington (2007)Google Scholar
  17. 17.
    Sánchez, L., Petkov, N., Alegre, E.: Classification of boar spermatozoid head images using a model intracellular density distribution. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 154–160. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Sánchez, L., Petkov, N., Alegre, E.: Statistical approach to boar semen head classification based on intracellular intensity distribution. In: Gagalowicz, A., Philips, W. (eds.) CAIP 2005. LNCS, vol. 3691, pp. 88–95. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Sánchez, L., Petkov, N., Alegre, E.: Statistical approach to boar semen evaluation using intracellular intensity distribution of head images. Cellular and Molecular Biology 52(6), 53–58 (2006)Google Scholar
  20. 20.
    Thurston, L.M., Watson, P.F., Mileham, A.J., Holt, W.V.: Morphologically distinct sperm subpopulations defined by Fourier shape descriptors in fresh ejaculates correlate with variation in boar semen quality following cryopreservation. Journal of Andrology 22(3), 382–394 (2001)PubMedGoogle Scholar
  21. 21.
    Verstegen, J., Iguer-Ouada, M., Onclin, K.: Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology 57, 149–179 (2002)CrossRefPubMedGoogle Scholar
  22. 22.
    Yi, W.J., Park, K.S., Paick, J.S.: Parameterized characterization of elliptic sperm heads using Fourier representation and wavelet transform. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, pp. 974–977 (1998)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lidia Sánchez
    • 1
  • Nicolai Petkov
    • 2
  1. 1.Department of Mechanical, Computing and Aerospace EngineeringsUniversity of LeónLeónSpain
  2. 2.Institute of Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands

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