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LVQ acrosome integrity assessment of boar sperm cells

University of León. University of Groningen. 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. Contents. 1. Introduction. 2. Vectorization.

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LVQ acrosome integrity assessment of boar sperm cells

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  1. University of León University of Groningen LVQ acrosome integrity assessment of boar sperm cells Nicolai Petkov1, Enrique Alegre2 Michael Biehl1, Lidia Sánchez2 1University of Groningen, The Netherlands 2University of León, Spain

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

  3. 1. Introduction

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

  5. Acrosome

  6. Acrosome reaction and fertilization

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

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

  9. 2. Vectorization

  10. Image acquisition

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

  12. Gradient computation

  13. Gradient magnitude Acrosome intact Acrosome reacted

  14. Gradient magnitude along head boundary

  15. Gradient magnitude along head boundary Acrosome intact Acrosome reacted

  16. 3. Learning Vector Quantization

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

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

  19. 4. Results

  20. Prototype profiles n = 1 m = 1 intact reacted m = 2 n = 1 i n t a c t reacted

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

  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

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