LVQ acrosome integrity assessment of boar sperm cells Nicolai Petkov 1, Enrique Alegre 2 Michael...

LVQ acrosome integrity assessment of LVQ acrosome integrity assessment of boar sperm cellsboar sperm cells

Nicolai Petkov1, Enrique Alegre2 Michael Biehl1, Lidia Sánchez2

1University of Groningen, The Netherlands2University of León, Spain

UniversityUniversity of Groningen of Groningen

UniversityUniversity of León of León

2

ContentsContents

2. Vectorization

3. Analysis by LVQ

4. Results

5. Conclusions

1. Introduction

1. Introduction1. Introduction

4

Quality assessment of semen, e.g. by measuringconcentration, motility, morphology, intracellular pattern

5

AcrosomeAcrosome

6

Acrosome reaction and fertilizationAcrosome reaction and fertilization

7

Acrosome stateAcrosome state

Acrosome reacted

Acrosome intact

Veterinary experts:

High fraction of acrosome-reacted cells means low fertilizing capacity

8

ApproachApproach

Fertilization potential estimation by

Automatic image analysis for

Estimation of the fraction of

acrosome-intact sperm cells

2. Vectorization2. Vectorization

10

Image acquisitionImage acquisition

11

cropping

Cell head segmentationCell head segmentation

histogram stretching

thresholding Opening & closing

12

Gradient computationGradient computation

13

Gradient magnitudeGradient magnitude

Acrosome intact

Acrosome reacted

14

Gradient magnitude along head boundaryGradient magnitude along head boundary

15

Gradient magnitude along head boundaryGradient magnitude along head boundary

Acrosome intact

Acrosome reacted

3. Learning Vector Quantization3. Learning Vector Quantization

17

Labeled data

Vectors of gradient magnitudes along the contour

Class membership

Labeled data P = 152

18

Select randomly example from D

Find nearest prototype vector (winner)

Update winner according to

LVQ1 trainingLVQ1 training

moves prototype towards/away from the actual example

4. Results4. Results

20

Prototype profilesPrototype profiles

intact reacted

i n t a c t reacted

m = 1

n = 1

m = 2

n = 1

21

Errors (8-fold cross validation)Errors (8-fold cross validation)

m and n prototypes of class 1 and 2, resp.

22

5. Conclusions5. 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