LVQ acrosome integrity assessment of boar sperm cells Nicolai Petkov 1, Enrique Alegre 2 Michael...
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Transcript of LVQ acrosome integrity assessment of boar sperm cells Nicolai Petkov 1, Enrique Alegre 2 Michael...
![Page 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.](https://reader030.fdocuments.us/reader030/viewer/2022032723/56649d095503460f949db6c6/html5/thumbnails/1.jpg)
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
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ContentsContents
2. Vectorization
3. Analysis by LVQ
4. Results
5. Conclusions
1. Introduction
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1. Introduction1. Introduction
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Quality assessment of semen, e.g. by measuringconcentration, motility, morphology, intracellular pattern
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AcrosomeAcrosome
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Acrosome reaction and fertilizationAcrosome reaction and fertilization
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Acrosome stateAcrosome state
Acrosome reacted
Acrosome intact
Veterinary experts:
High fraction of acrosome-reacted cells means low fertilizing capacity
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ApproachApproach
Fertilization potential estimation by
Automatic image analysis for
Estimation of the fraction of
acrosome-intact sperm cells
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2. Vectorization2. Vectorization
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Image acquisitionImage acquisition
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cropping
Cell head segmentationCell head segmentation
histogram stretching
thresholding Opening & closing
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Gradient computationGradient computation
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Gradient magnitudeGradient magnitude
Acrosome intact
Acrosome reacted
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Gradient magnitude along head boundaryGradient magnitude along head boundary
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Gradient magnitude along head boundaryGradient magnitude along head boundary
Acrosome intact
Acrosome reacted
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3. Learning Vector Quantization3. Learning Vector Quantization
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Labeled data
Vectors of gradient magnitudes along the contour
Class membership
Labeled data P = 152
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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
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4. Results4. Results
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Prototype profilesPrototype profiles
intact reacted
i n t a c t reacted
m = 1
n = 1
m = 2
n = 1
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Errors (8-fold cross validation)Errors (8-fold cross validation)
m and n prototypes of class 1 and 2, resp.
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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