Publications

@article{alegre_assessment_2013,

title = {Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ},

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

url = {https://www.sciencedirect.com/science/article/pii/S0169260713001478},

year  = {2013},

date = {2013-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {111},

number = {3},

pages = {525–536},

abstract = {This paper presents a digital image processing method to assess the acrosome state of boar spermatozoa heads. Using grayscale images labeled with fluorescent data, the sperm heads are segmented, and multiple inner contours are generated using a logarithmic distance function. Local texture features are computed for these contours, and classification performance is evaluated using Relevance Learning Vector Quantization, class conditional means, and KNN with cross-validation. The best results are achieved with gradient magnitude data, yielding a test error of only 1%, outperforming previous methods and demonstrating the potential for automated veterinary applications.},

note = {Publisher: Elsevier},

keywords = {digital image processing, machine learning, Sperm Analysis, veterinary science},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez-robles_acrosome_2011,

title = {Acrosome evaluation of spermatozoa cells using sift and classical texture descriptors},

author = {Laura Fernández-Robles and Maite García-Ordás and Diego García-Ordás and Óscar García-Olalla Olivera and Enrique Alegre},

url = {https://portalcientifico.unileon.es/documentos/6660aac4241b8f26a79c807a},

year  = {2011},

date = {2011-01-01},

journal = {Actas de las XXXII Jornadas de Automática, Escuela Técnica Superior de Ingeniería, Univesidad de Sevilla: Sevilla, 7 al 9 de septiembre de 2011},

pages = {84},

abstract = {Automatic assessment of sperm quality is an important 

challenge in the veterinary field. In this 

paper, we explore how to best describe the acrosomes 

of boar spermatozoa using image analysis 

to automatically classify them as intact or damaged. 

Our proposal is to characterize the acrosomes 

in terms of their membrane integrity using 

texture descriptors and compare them with descriptors 

based on local invariant features, particularly, 

Scale Invariant Feature Transform (SIFT) 

method. On the one hand, we use Zernike moments 

and Haralick features extracted from the 

original image and from the coefficients of the Discrete 

Wavelet Transform. On the other hand, the 

heads’ features are distinctively described by SIFT, 

a method based on detecting local points of interest. 

Classification using kNN shows that the best 

results were obtained by SIFT, with an overall hit 

rate of 84.64% and, what is more important, a 

higher hit rate in the damaged (92.96%) than in 

the intact class (76.15%). These results make this 

descriptor very attractive for the veterinary community.},

note = {Publisher: Universidad de Sevilla},

keywords = {acrosome integrity, image processing, SIFT, Sperm Analysis, veterinary science},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_comparison_2009,

title = {Comparison of supervised and unsupervised methods to classify boar acrosomes using texture descriptors},

author = {Enrique Alegre and Víctor González-Castro and Manuel Castejón-Limas},

url = {https://ieeexplore.ieee.org/abstract/document/5342859},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {2009 International Symposium ELMAR},

pages = {65–70},

abstract = {This work compares supervised and unsupervised methods for classifying boar sperm head images based on membrane integrity. Five texture descriptors were tested, and classification was performed using LDA, QDA, k-NN, and Neural Networks. Results indicate that unsupervised methods outperform supervised ones, achieving a lower error rate of 6.11% compared to 9%.},

note = {Publisher: IEEE},

keywords = {Image classification, Sperm Analysis, Supervised vs. Unsupervised Learning, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{sanchez-gonzalez_classification_2005,

title = {Classification of boar spermatozoid head images using a model intracellular density distribution},

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

url = {https://link.springer.com/chapter/10.1007/11578079_17},

year  = {2005},

date = {2005-01-01},

journal = {Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings 10},

pages = {154–160},

abstract = {A novel method is proposed to classify boar spermatozoid heads based on intracellular intensity distribution. A model distribution is created from normal samples, and deviations are used for classification. The decision criterion minimizes classification errors, achieving a global error of 20.40%.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {Biomedical Imaging, Image classification, machine learning, Pattern Recognition, Sperm Analysis},

pubstate = {published},

tppubtype = {article}

}