Computers in Biology and Medicine
Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images
Similarity-Based Clustering
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images
Computer Methods and Programs in Biomedicine
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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.