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Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability

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International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 (SOCO 2016, CISIS 2016, ICEUTE 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

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Abstract

This paper employs well-known techniques as Support Vector Machines and Neural Networks in order to classify images of boar sperm cells. Acrosome integrity gives information about if a sperm cell is able to fertilize an oocyte. If the acrosome is intact, the fertilization is possible. Otherwise, if a sperm cell has already reacted and has lost its acrosome or even if it is going through the capacitation process, such sperm cell has lost its capability to fertilize. Using a set of descriptors already proposed to describe the acrosome state of a boar sperm cell image, two different classifiers are considered. Results show the classification accuracy improves previous results.

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Notes

  1. 1.

    http://pitia.unileon.es/varp/node/361.

References

  1. Alegre, E., Biehl, M., Petkov, N., Sanchez, L.: Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Comput. Biol. Med. 38(4), 461–468 (2008)

    Article  Google Scholar 

  2. Alegre, E., Biehl, M., Petkov, N., Sanchez, L.: Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ. Comput. Methods Programs Biomed. 111, 525–536 (2013)

    Article  Google Scholar 

  3. Alegre, E., García-Olalla, O., González-Castro, V., Joshi, S.: Boar spermatozoa classification using longitudinal and transversal profiles (LTP) descriptor in digital images. In: Aggarwal, J.K., Barneva, R.P., Brimkov, V.E., Koroutchev, K.N., Korutcheva, E.R. (eds.) IWCIA 2011. LNCS, vol. 6636, pp. 410–419. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21073-0_36

    Chapter  Google Scholar 

  4. Alegre, E., Garcia-Ordas, M., Gonzalez-Castro, V., Karthikeyan, S.: Vitality assessment of boar sperm using NCSR texture descriptor in digital images. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669. Springer, Heidelberg (2011)

    Google Scholar 

  5. Alegre, E., Gonzalez-Castro, V., Alaiz-Rodriguez, R., Garcia-Ordas, M.: Texture and moments-based classification of the acrosome integrity of boar spermatozoa images. Comput. Methods Programs Biomed. 108(2), 873–881 (2012)

    Article  Google Scholar 

  6. Bijar, A., Pealver-Benavent, A., Mikaeili, M., Khayati, R.: Fully automatic identification and discrimination of sperm parts in microscopic images of stained human semen smear. J. Biomed. Sci. Eng. 5, 384–395 (2012)

    Article  Google Scholar 

  7. Boland, M., Murphy, R.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of hela cells. Bioinformatics 17(12), 1213–1223 (2001)

    Article  Google Scholar 

  8. Chan, J., Krause, W., Bohring, C.: Computer-assisted analysis of sperm morphology with the aid of lectin staining. Andrologia 34(6), 379–383 (2002)

    Article  Google Scholar 

  9. Downing, T., Garner, D., Ericsson, S., Redelman, D.: Metabolic toxicity of fluorescent stains on thawed cryopreserved bovine sperm cells. J. Histochem. Cytochem. 39(4), 485–489 (1991)

    Article  Google Scholar 

  10. Fazeli, A., Hage, W., Cheng, F.P., Voorhout, W., Marks, A., Bevers, M., Colenbrander, B.: Acrosome-intact boar spermatozoa initiate binding to the homologous zona pellucida in vitro. Biol. Reprod. 56, 430–438 (1997)

    Article  Google Scholar 

  11. Garcia-Olalla, O., Alegre, E., Fernandez-Robles, L., Malm, P., Bengtsson, E.: Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors. Comput. Methods Programs Biomed. 120(1), 49–64 (2015)

    Article  Google Scholar 

  12. Gonzalez-Castro, V., Alaiz-Rodriguez, R., Alegre, E.: Class distribution estimation based on the hellinger distance. Inf. Sci. 218, 146–164 (2013)

    Article  Google Scholar 

  13. González-Castro, V., Alegre, E., García-Olalla, O., García-Ordás, D., García-Ordás, M.T., Fernández-Robles, L.: Curvelet-based texture description to classify intact and damaged boar spermatozoa. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012. LNCS, vol. 7325, pp. 448–455. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31298-4_53

    Chapter  Google Scholar 

  14. Linneberg, C., Salamon, P., Svarer, C., Hansen, L.: Towards semen quality assessment using neural networks. In: Proceedings of IEEE Neural Networks for Signal Processing IV (1994)

    Google Scholar 

  15. Neuwinger, J., Behre, H., Nieschlag, E.: External quality control in the andrology laboratory: an experimental multicenter trial. Fertil. Steril. 54(2), 308–314 (1990)

    Article  Google Scholar 

  16. Oliva-Hernandez, J., Corcuera, B., Perez-Gutierrez, J.: Epidermal growth factor (EGF) effects on boar sperm capacitation. Reprod. Domest. Anim. 40, 353 (2005)

    Google Scholar 

  17. Petkov, N., Alegre, E., Biehl, M., Sanchez, L.: LVQ acrosome integrity assessment of boar sperm cells. In: Proceedings of Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications (CompIMAGE) (2006)

    Google Scholar 

  18. Petrunkina, A., Petzoldt, R., Stahlberg, S., Pfeilsticker, J., Beyerbach, M., Bader, H., Topfer-Petersen, E.: Sperm-cell volumetric measurements as parameters in bull semen function evaluation: correlation with nonreturn rate. Andrologia 33, 360–367 (2001)

    Article  Google Scholar 

  19. 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. Reprod. Domest. Anim. 40, 356 (2005)

    Google Scholar 

  20. Sanchez, L.: Boar sperm cell classification using digital image processing. Ph.D. thesis, University of Leon, Spain (2007)

    Google Scholar 

  21. Verstegen, J., Iguer-Ouada, M., Onclin, K.: Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology 57, 149–179 (2002)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical, Computer and Aerospace Engineering, University of León, León, Spain

    Lidia Sánchez, Javier Alfonso-Cendón & Hilde Pérez

  2. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Héctor Quintian & Emilio Corchado

Authors
  1. Lidia Sánchez
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  2. Héctor Quintian
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  3. Javier Alfonso-Cendón
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  4. Hilde Pérez
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  5. Emilio Corchado
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Corresponding author

Correspondence to Lidia Sánchez .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Sánchez, L., Quintian, H., Alfonso-Cendón, J., Pérez, H., Corchado, E. (2017). Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

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