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Statistical approach to boar semen evaluation using
intracellular intensity distribution of head images
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Lidia Sanchez
Univ. Leon, Spain

Abstract:

We propose a method for the classification of boar sperm heads based on
their intracellular intensity distributions observed in microscopic
images. The image pre-processing comprises segmentation of cell heads and
normalization for brightness, contrast and size. Next, we define a model
distribution of head intracellular intensity using a set of head images
assumed to be alive by veterinary experts. We now consider two other sets
of cell head images, one formed by heads assumed to be alive by experts
and another formed by cells which present some abnormalities in their
cytoplasm densities and are considered as dead by the experts. We define a
measure of deviation from the model intensity distribution and for each
head image of the two test sets, we compute the deviation from the model.
While the distributions of deviation values for alive and dead cells
overlap, it is possible to choose an optimal value of a decision criterion
for single cell classification in such a way that the error made in the
estimation of the fraction of alive cells in a sample is minimal. In the
range of interesting values of the fraction of alive cells, the standard
deviation of the fraction estimation error for samples of 100 head images
is smaller than 0.04. Thus, in 95% of the cases the real value of the
fraction of alive cells in a sample will be within 8% of the estimation
made according to the proposed method. This result satisfies the
requirements of veterinary practice.