Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ

Authors:
Enrique Alegre;Michael Biehl;Nicolai Petkov;Lidia Sánchez
Affiliations:
Department of Electrical, Systems and Automatic Engineering, University of León, 24071 Leon, Spain;Institute of Mathematics and Computing Science, University of Groningen, The Netherlands;Institute of Mathematics and Computing Science, University of Groningen, The Netherlands;Department of Mechanical, Computing and Aerospace Engineerings, University of León, Spain
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

We consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-damaged (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for each head. As a feature vector we use the gradient magnitude along the contour of the sperm head. We apply learning vector quantization (LVQ) to the feature vectors obtained for 320 heads that were labelled as intact or damaged using stains. A LVQ system with four prototypes (two for each class) allows us to classify cells with an overall test error of 6.8%. This is considered to be sufficient for semen quality control in an artificial insemination center.