Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ

Computers in Biology and Medicine

Volumn 38, Issue 4, 2008, Pages 461-468

  Alegre, Enrique ^a   Biehl, Michael ^b   Petkov, Nicolai ^b   Sánchez, Lidia ^a  

^a UNIVERSITY OF LEÓN   (Spain)

^b UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

Acrosome assessment; Boar sperm cells; Classification; Digital image processing; Gradient magnitude contour; LVQ; Phase contrast microscope image

Indexed keywords

BODY FLUIDS; ESTIMATION; GRADIENT METHODS; IMAGE PROCESSING; VECTOR QUANTIZATION;

ACROSOME ASSESSMENTS; BOAR SPERM CELLS; GRADIENT MAGNITUDE CONTOUR; PHASE-CONTRAST MICROSCOPE IMAGE;

CELLS;

ACROSOME; ARTICLE; COMPUTER AIDED DESIGN; COMPUTER SYSTEM; FERTILIZATION; IMAGE PROCESSING; NONHUMAN; PRIORITY JOURNAL; SEMEN ANALYSIS; SPERMATOZOON; SWINE;

ACROSOME; ACROSOME REACTION; ANIMALS; EXPERT SYSTEMS; IMAGE PROCESSING, COMPUTER-ASSISTED; INSEMINATION, ARTIFICIAL; MALE; MICROSCOPY, PHASE-CONTRAST; SOFTWARE; SPERM CAPACITATION; SPERMATOZOA; SWINE;

EID: 40949141347     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2008.01.005     Document Type: Article

References (27)

1. Verstegen J., Iguer-Ouada M., and Onclin K. Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology 57 (2002) 149-179
2. C. Linneberg, P. Salamon, C. Svarer, L. Hansen, Towards semen quality assessment using neural networks, in: Proceedings of the IEEE Neural Networks for Signal Processing, vol. IV, 1994, pp. 509-517.
3. Suzuki T., Shibahara H., Tsunoda H., Hirano Y., Taneichi A., Obara H., Takamizawa S., and Sato I. Comparison of the sperm quality analyzer IIC variables with the computer-aided sperm analysis estimates. Int. J. Androl. 25 (2002) 49-54
4. Beletti M., and Costa L. A systematic approach to multi-species sperm morphometrical characterization. Anal. Quant. Cytol. Histol. 25 2 (2003) 97-107
5. Hirai M., Boersma A., Hoeflich A., Wolf E., Foll J., Aumuller T., and 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 (2001) 104-110
6. Quintero A., Rigaub T., and Rodríguez J. Regression analyses and motile sperm subpopulation structure study as improving tools in boar semen quality analysis. Theriogenology 61 (2004) 673-690
7. Ostermeier G., Sargeant G., Yandell T., and Parrish J. Measurement of bovine sperm nuclear shape using Fourier harmonic amplitudes. J. Androl. 22 (2001) 584-594
8. Rijsselaere T., Soom A.V., Hoflack G., Maes D., and de Kruif A. Automated sperm morphometry and morphology analysis of canine semen by the Hamilton-Thorne analyser. Theriogenology 62 7 (2004) 1292-1306
9. Beletti M., Costa L., and Viana M. A comparison of morphometric characteristics of sperm from fertile Bos taurus and Bos indicus bulls in Brazil. Anim. Reprod. Sci. 85 (2005) 105-116
10. Beletti M., Costa L., and Viana M. A spectral framework for sperm shape characterization. Comput. Biol. Med. 35 6 (2005) 463-473
11. R. Fernández, E. Alegre, L. Sánchez, F. Tejerina, R. González, J. Domínguez, Automatic detection and measuring of distal droplets in boar spermatozoa using computer vision techniques, in: Proceedings of the 9th Annual Conference European Society for Domestic Animal Reproduction, ESDAR 2005, Reproduction in Domestic Animals, vol. 40, 2005, p. 390.
12. L. Sánchez, N. Petkov, E. Alegre, Statistical approach to boar semen head classification based on intracellular intensity distribution, in: A. Gagalowicz, W. Philips (Eds.), Proceedings of the International Conference on Computer Analysis of Images and Patterns, CAIP 2005, Lecture Notes in Computer Science, vol. 3691, Springer, Berlin, Heidelberg, 2005, pp. 88-95.
13. L. Sánchez, N. Petkov, E. Alegre, Classification of boar spermatozoid head images using a model intracellular density distribution, in: M. Lazo, A. Sanfeliu (Eds.), Progress in Pattern Recognition, Image Analysis and Applications: Proceedings of the 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Lecture Notes in Computer Science, vol. 3773, Springer, Berlin, Heidelberg, 2005, pp. 154-160.
14. M. Biehl, P. Pasma, M. Pijl, L. Sanchez, N. Petkov, Classification of boar sperm head images using learning vector quantization, in: M. Verleysen (Ed.), Proceedings of the European Symposium on Artificial Neural Networks (ESANN), Brugge, April 26-28, 2006, pp. 545-550. d-side, Evere, Belgium, 2006.
15. Harrison R., and Vickers E. Use of fluorescent probes to assess membrane integrity in mammalian spermatozoa. J. Reprod. Fertility 88 (1990) 343-352
16. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9 (1979) 62-66
17. Alegre E., Fernández R., Sánchez L., Rodríguez V., and Domínguez J. Digital image segmentation methods for automatic quality evaluation of boar semen. Reprod. Domestic Anim. 40 (2005) 392
18. T. Alonso, E. Alegre, L. Sánchez, F. Tejerina, J. Domínguez, Segmentación y etiquetado automático de cabezas de espermatoides de verraco, in: XXVII Jornadas de Automática, Almería, Septembre 2006.
19. L. Schwartz, Théorie des Distributions, Volume I, II of Actualités scientifiques et industrielle, L'Institute de Mathématique de l'Université de Strasbourg, 1950-1951.
20. Canny J.F. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8 6 (1986) 679-698
21. Tagare H., and de Figueiredo R. On the localization performance measure and optimal edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 12 12 (1990) 1186-1190
22. Grigorescu C., Petkov N., and Westenberg M.A. Contour and boundary detection improved by surround suppression of texture edges. Image Vision Comput. 22 8 (2004) 609-622
23. Duda R., Hart P., and Stork D. Pattern Classification (2001), Wiley, New York
24. Kohonen T. Self-organizing Maps (1995), Springer, Berlin
25. Neural Networks Research Centre, Bibliography on the self-organizing map (SOM) and learning vector quantization (LVQ), Helsinki University of Technology, 2002.
26. Sato A., and Yamada K. Generalized learning vector quantization. In: Tesauro G., Touretzky D., and Leen T. (Eds). Advances in Neural Information Processing Systems vol. 7 (1995), MIT Press, Cambridge 423-429
27. Hammer B., and Villmann T. Generalized relevance learning vector quantization. Neural Networks 15 (2002) 1059-1068