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

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.

Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors

“…Most of the works are also based on texture description. Sánchez et al [24] used the intensity distribution of the cytoplasm densities of the cells whereas [25] adds standard deviation information to the Local Binary Pattern (LBP) descriptor, [26] presents a new textural descriptor called NCSR and [27] shows the performance of LTP texture descriptor.…”