Publications
2012
1.
Alegre, Enrique; González-Castro, Víctor; Alaiz-Rodríguez, Rocío; García-Ordás, María Teresa
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images Artículo de revista
En: Computer Methods and Programs in Biomedicine, vol. 108, no 2, pp. 873–881, 2012, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, boar semen, Discrete Wavelet Transform, Invariant Moments, k-Nearest Neigbours, neural networks, texture descriptors
@article{alegre_texture_2012,
title = {Texture and moments-based classification of the acrosome integrity of boar spermatozoa images},
author = {Enrique Alegre and Víctor González-Castro and Rocío Alaiz-Rodríguez and María Teresa García-Ordás},
url = {https://www.sciencedirect.com/science/article/pii/S0169260712000314},
year = {2012},
date = {2012-01-01},
journal = {Computer Methods and Programs in Biomedicine},
volume = {108},
number = {2},
pages = {873–881},
abstract = {This paper addresses the automated assessment of sperm quality in the veterinary field by using image analysis to categorize boar spermatozoa acrosomes as intact or damaged. The acrosomes are characterized using texture features derived from first-order statistics, co-occurrence matrices, and Discrete Wavelet Transform coefficients. The study compares texture-based descriptors with moment-based ones and finds that texture descriptors outperform moment-based descriptors, achieving a classification accuracy of 94.93% using Multilayer Perceptron and k-Nearest Neighbors classifiers, offering a promising approach for veterinarians.},
note = {Publisher: Elsevier},
keywords = {acrosome integrity, boar semen, Discrete Wavelet Transform, Invariant Moments, k-Nearest Neigbours, neural networks, texture descriptors},
pubstate = {published},
tppubtype = {article}
}
This paper addresses the automated assessment of sperm quality in the veterinary field by using image analysis to categorize boar spermatozoa acrosomes as intact or damaged. The acrosomes are characterized using texture features derived from first-order statistics, co-occurrence matrices, and Discrete Wavelet Transform coefficients. The study compares texture-based descriptors with moment-based ones and finds that texture descriptors outperform moment-based descriptors, achieving a classification accuracy of 94.93% using Multilayer Perceptron and k-Nearest Neighbors classifiers, offering a promising approach for veterinarians.
2.
Fernández-Robles, Laura; Olivera, Óscar García-Olalla; García-Ordás, Maite; García-Ordás, Diego; Alegre, Enrique
Svm approach to classify boar acrosome integrity of a multi-features surf description Artículo de revista
En: Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012, pp. 121, 2012, (Publisher: Universidade de Vigo).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, Multi-feature Classification, SVM
@article{fernandez-robles_svm_2012,
title = {Svm approach to classify boar acrosome integrity of a multi-features surf description},
author = {Laura Fernández-Robles and Óscar García-Olalla Olivera and Maite García-Ordás and Diego García-Ordás and Enrique Alegre},
url = {https://dialnet.unirioja.es/servlet/articulo?codigo=8886078},
year = {2012},
date = {2012-01-01},
journal = {Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012},
pages = {121},
abstract = {Actas de las XXXIII Jornadas de Automática},
note = {Publisher: Universidade de Vigo},
keywords = {acrosome integrity, Multi-feature Classification, SVM},
pubstate = {published},
tppubtype = {article}
}
Actas de las XXXIII Jornadas de Automática
2011
3.
Fernández-Robles, Laura; García-Ordás, Maite; García-Ordás, Diego; Olivera, Óscar García-Olalla; Alegre, Enrique
Acrosome evaluation of spermatozoa cells using sift and classical texture descriptors Artículo de revista
En: Actas de las XXXII Jornadas de Automática, Escuela Técnica Superior de Ingeniería, Univesidad de Sevilla: Sevilla, 7 al 9 de septiembre de 2011, pp. 84, 2011, (Publisher: Universidad de Sevilla).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, image processing, SIFT, Sperm Analysis, veterinary science
@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}
}
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.
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.
2007
4.
González, Maribel; Alegre, Enrique; Alaiz-Rodríguez, Rocío; Sánchez-González, Lidia
Acrosome integrity classification of boar spermatozoon images using dwt and texture descriptors Artículo de revista
En: Computational Vision and Medical Image Processing: VipIMAGE, vol. 2007, 2007.
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, contour description, early fusion, fourier shape descriptor, LBP, SVM, texture description, wavelet
@article{gonzalez_acrosome_2007,
title = {Acrosome integrity classification of boar spermatozoon images using dwt and texture descriptors},
author = {Maribel González and Enrique Alegre and Rocío Alaiz-Rodríguez and Lidia Sánchez-González},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449828/Acrosome_integrity_assessment_of_boar_sp20160405-3183-jdniiw-libre.pdf?1459894375=&response-content-disposition=inline%3B+filename%3DAcrosome_integrity_assessment_of_boar_sp.pdf&Expires=1738604441&Signature=gCki53PLr5Uqz1IsOZ87L788ljr1cPDhvd3XAIPiXXJyhy7gT1U0WIFjenpIGpKsNIg1lei0Y9wxLIssqiUqYYi2BrXDLX8qxHOSVnNjAj8bmBUVTWeiFHnHvMPbg–6ZzHG71Dj0RkarOCf1~C~OvGTQbjmSLusV5afdpSCJRuBd2eVbcFy4NGFcTSRMxPZGwJO-t87Aheo846zp-rUxOlkSN4YluJiuov6VhGnufaa4PfmwWgMSUxod9HGpYagpjvzk~RT24b73pZKITpgQqaeIrgE9O27~kYabvFZ3wWr8c0wEmGIBbj8doWz59ibPXF7GBaIzZXZj1TyWyvbQA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2007},
date = {2007-01-01},
journal = {Computational Vision and Medical Image Processing: VipIMAGE},
volume = {2007},
abstract = {This study focuses on classifying boar sperm as acrosome-intact or acrosome-damaged using grayscale images from phase-contrast microscopy. By combining shape and texture descriptors with a Support Vector Machine (SVM), the authors achieve an F-Score of 0.9913, outperforming previous methods. This work highlights the importance of sperm head contour information and the effectiveness of early fusion techniques in sperm classification, making it a significant advancement in the field.},
keywords = {acrosome integrity, contour description, early fusion, fourier shape descriptor, LBP, SVM, texture description, wavelet},
pubstate = {published},
tppubtype = {article}
}
This study focuses on classifying boar sperm as acrosome-intact or acrosome-damaged using grayscale images from phase-contrast microscopy. By combining shape and texture descriptors with a Support Vector Machine (SVM), the authors achieve an F-Score of 0.9913, outperforming previous methods. This work highlights the importance of sperm head contour information and the effectiveness of early fusion techniques in sperm classification, making it a significant advancement in the field.