Publications

@article{alegre_automatic_2008,

title = {Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ},

author = {Enrique Alegre and Michael Biehl and Nicolai Petkov and Lidia Sánchez-González},

url = {https://www.sciencedirect.com/science/article/pii/S0010482508000103},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {Computers in Biology and Medicine},

volume = {38},

number = {4},

pages = {461–468},

abstract = {This paper presents an automatic method for classifying boar sperm cells as acrosome-intact or acrosome-damaged using phase-contrast microscopy images. Sperm heads are segmented, and feature vectors based on gradient magnitude along the contour are extracted. Learning Vector Quantization (LVQ) is applied to classify 320 labeled sperm heads, achieving a 6.8% test error, which is sufficient for semen quality control in artificial insemination.},

note = {Publisher: Pergamon},

keywords = {artificial insemination, machine learning, phase-contrast microscopy, sperm classification},

pubstate = {published},

tppubtype = {article}

}

@article{sanchez-gonzalez_statistical_2005,

title = {Statistical approach to boar semen head classification based on intracellular intensity distribution},

author = {Lidia Sánchez-González and Nicolai Petkov and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/11556121_12},

year  = {2005},

date = {2005-01-01},

journal = {International Conference on Computer Analysis of Images and Patterns},

pages = {88–95},

abstract = {This technique estimates the fraction of boar sperm heads exhibiting a normal intracellular density pattern, as defined by veterinary experts. It offers a potential alternative to costly staining methods for sperm capacitation. The method involves training a model using images classified as normal, similar to normal, and abnormal. The deviation of each sperm head from the model is used to calculate a conditional probability, allowing for accurate estimation of normal sperm fractions, with an error below 0.25 in 89% of the test samples.},

note = {Publisher: Springer Berlin Heidelberg Berlin, Heidelberg},

keywords = {Digital Estimation, image processing, Sperm Capacitation, sperm classification, Veterinary Analysis},

pubstate = {published},

tppubtype = {article}

}