Classification of Boar Spermatozoid Head Images Using a Model Intracellular Density Distribution

  • Lidia Sánchez
  • Nicolai Petkov
  • Enrique Alegre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


We propose a novel classification method to identify boar spermatozoid heads which present an intracellular intensity distribution similar to a model. From semen sample images, head images are isolated and normalized. We define a model intensity distribution averaging a set of head images assumed as normal by veterinary experts. Two training sets are also formed: one with images that are similar to the model and another with non-normal head images according to experts. Deviations from the model are computed for each set, obtaining low values for normal heads and higher values for assumed as non-normal heads. There is also an overlapped area. The decision criterion is determined to minimize the sum of the obtained false rejected and false acceptance errors. Experiments with a test set of normal and non-normal head images give a global error of 20.40%. The false rejection and the false acceptance rates are 13.68% and 6.72% respectively.


Decision Criterion False Acceptance Rate Normal Head Head Image Normal Density Distribution 
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  1. 1.
    Gravance, C., Garner, D., Pitt, C., Vishwanath, R., Sax-Gravance, S., Casey, P.: Replicate and technician variation associated with computer aided bull sperm head morphometry analysis (ASMA). International Journal of Andrology 22, 77–82 (1999)CrossRefGoogle Scholar
  2. 2.
    Hirai, M., Boersma, A., Hoeflich, A., Wolf, E., Foll, J., Aumuller, T., Braun, J.: Objectively measured sperm motility and sperm head morphometry in boars (Sus scrofa): relation to fertility and seminal plasma growth factors. J. Androl. 22, 104–110 (2001)Google Scholar
  3. 3.
    Wijchman, J., Wolf, B.D., Graafe, R., Arts, E.: Variation in semen parameters derived from computer-aided semen analysis, within donors and between donors. J. Androl. 22, 773–780 (2001)Google Scholar
  4. 4.
    Suzuki, T., Shibahara, H., Tsunoda, H., Hirano, Y., Taneichi, A., Obara, H., Takamizawa, S., Sato, I.: Comparison of the sperm quality analyzer IIC variables with the computer-aided sperm analysis estimates. International Journal of Andrology 25, 49–54 (2002)CrossRefGoogle Scholar
  5. 5.
    Rijsselaere, T., Soom, A.V., Hoflack, G., Maes, D., de Kruif, A.: Automated sperm morphometry and morphology analysis of canine semen by the Hamilton-Thorne analyser. Theriogenology 62, 1292–1306 (2004)CrossRefGoogle Scholar
  6. 6.
    Verstegen, J., Iguer-Ouada, M., Onclin, K.: Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology 57, 149–179 (2002)CrossRefGoogle Scholar
  7. 7.
    Linneberg, C., Salamon, P., Svarer, C., Hansen, L.: Towards semen quality assessment using neural networks. In: Proc. IEEE Neural Networks for Signal Processing IV, pp. 509–517 (1994)Google Scholar
  8. 8.
    Garrett, C., Baker, H.: A new fully automated system for the morphometric analysis of human sperm heads. Fertil. Steril. 63, 1306–1317 (1995)Google Scholar
  9. 9.
    Ostermeier, G., Sargeant, G., Yandell, T., Parrish, J.: Measurement of bovine sperm nuclear shape using Fourier harmonic amplitudes. J. Androl. 22, 584–594 (2001)Google Scholar
  10. 10.
    Alegre, E., Sánchez, L., Aláiz, R., Dominguez-Fernández, J.: Utilización de momentos estadísticos y redes neuronales en la clasificación de cabezas de espermatozoides de verraco. In: XXV Jornadas de Automática (2004)Google Scholar
  11. 11.
    Beletti, M., Costa, L., Viana, M.: A comparison of morphometric characteristics of sperm from fertile Bos taurus and Bos indicus bulls in Brazil. Animal Reproduction Science 85, 105–116 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lidia Sánchez
    • 1
  • Nicolai Petkov
    • 2
  • Enrique Alegre
    • 1
  1. 1.Department of Electrical and Electronics EngineeringUniversity of LeónLeónSpain
  2. 2.Institute of Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands

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