Classification of Boar Spermatozoid Head Images Using a Model Intracellular Density Distribution

  • Lidia Sánchez
  • Nicolai Petkov
  • Enrique Alegre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

We propose a novel classification method to identify boar spermatozoid heads which present an intracellular intensity distribution similar to a model. From semen sample images, head images are isolated and normalized. We define a model intensity distribution averaging a set of head images assumed as normal by veterinary experts. Two training sets are also formed: one with images that are similar to the model and another with non-normal head images according to experts. Deviations from the model are computed for each set, obtaining low values for normal heads and higher values for assumed as non-normal heads. There is also an overlapped area. The decision criterion is determined to minimize the sum of the obtained false rejected and false acceptance errors. Experiments with a test set of normal and non-normal head images give a global error of 20.40%. The false rejection and the false acceptance rates are 13.68% and 6.72% respectively.

Keywords

Decision Criterion False Acceptance Rate Normal Head Head Image Normal Density Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lidia Sánchez
    • 1
  • Nicolai Petkov
    • 2
  • Enrique Alegre
    • 1
  1. 1.Department of Electrical and Electronics EngineeringUniversity of LeónLeónSpain
  2. 2.Institute of Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands

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