1. LVQ acrosome integrity assessment of boar sperm cells Nicolai Petkov 1, Enrique Alegre 2 Michael Biehl 1, Lidia Sánchez 2 1 University of Groningen, The Netherlands 2 University of León, Spain University of Groningen University of León

2. 2 Contents 2. Vectorization 3. Analysis by LVQ 4. Results 5. Conclusions 1. Introduction

4. 4 Quality assessment of semen, e.g. by measuring concentration, motility, morphology, intracellular pattern

5. 5 Acrosome

6. 6 Acrosome reaction and fertilization

7. 7 Acrosome state Acrosome reacted Acrosome intact Veterinary experts: High fraction of acrosome- reacted cells means low fertilizing capacity

8. 8 Approach Fertilization potential estimation by Automatic image analysis for Estimation of the fraction of acrosome-intact sperm cells

9. 2. Vectorization

10. 10 Image acquisition

11. 11 cropping Cell head segmentation histogram stretching thresholding Opening & closing

12. 12 Gradient computation

13. 13 Gradient magnitude Acrosome intact Acrosome reacted

14. 14 Gradient magnitude along head boundary

15. 15 Gradient magnitude along head boundary Acrosome intact Acrosome reacted

16. 3. Learning Vector Quantization

17. 17 Labeled data Vectors of gradient magnitudes along the contour Class membership Labeled dataP = 152

18. 18  Select randomly example from D  Find nearest prototype vector (winner)  Update winner according to LVQ1 training moves prototype towards/away from the actual example

19. 4. Results

20. 20 Prototype profiles intact reacted m = 1 n = 1 m = 2 n = 1

21. 21 Errors (8-fold cross validation) m and n prototypes of class 1 and 2, resp.

22. 22 5. Conclusions  Gradient magnitude along the cell head contour is a useful feature vector  LVQ1 with 3 prototypes (2 for class 1) produces (training and test) errors of 0.165  Veterinary experts call this sufficient for semen quality control in an artificial insemination center