Publications
2013
1.
Olivera, Oscar García-Olalla; Alegre, Enrique; Fernández-Robles, Laura; García-Ordás, María Teresa
Vitality assessment of boar sperm using an adaptive LBP based on oriented deviation Artículo de revista
En: Computer Vision-ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012, Revised Selected Papers, Part I 11, pp. 61–72, 2013, (Publisher: Springer Berlin Heidelberg).
Resumen | Enlaces | BibTeX | Etiquetas: local binary pattern, Sperm Vitality, support vector machine, texture descriptors
@article{garcia-olalla_olivera_vitality_2013,
title = {Vitality assessment of boar sperm using an adaptive LBP based on oriented deviation},
author = {Oscar García-Olalla Olivera and Enrique Alegre and Laura Fernández-Robles and María Teresa García-Ordás},
url = {https://link.springer.com/chapter/10.1007/978-3-642-37410-4_6},
year = {2013},
date = {2013-01-01},
journal = {Computer Vision-ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012, Revised Selected Papers, Part I 11},
pages = {61–72},
abstract = {This paper proposes a new method to assess sperm vitality using a hybrid combination of local and global texture descriptors. A new adaptive local binary pattern (ALBP) descriptor, enhanced with oriented standard deviation information, is introduced as ALBPS, achieving better performance in sperm vitality assessment with an accuracy of 81.88%. In addition, the global description of sperm heads was evaluated with various classical texture algorithms, where the combination of Wavelet transform and Haralick feature extraction (WCF13) yielded the best results. The hybrid approach combining ALBPS and WCF13 achieved superior performance, with the best result being a F-Score of 0.886 and an accuracy of 85.63%. This method represents a significant improvement in sperm vitality classification.},
note = {Publisher: Springer Berlin Heidelberg},
keywords = {local binary pattern, Sperm Vitality, support vector machine, texture descriptors},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a new method to assess sperm vitality using a hybrid combination of local and global texture descriptors. A new adaptive local binary pattern (ALBP) descriptor, enhanced with oriented standard deviation information, is introduced as ALBPS, achieving better performance in sperm vitality assessment with an accuracy of 81.88%. In addition, the global description of sperm heads was evaluated with various classical texture algorithms, where the combination of Wavelet transform and Haralick feature extraction (WCF13) yielded the best results. The hybrid approach combining ALBPS and WCF13 achieved superior performance, with the best result being a F-Score of 0.886 and an accuracy of 85.63%. This method represents a significant improvement in sperm vitality classification.
2012
2.
Olivera, Óscar García-Olalla; García-Ordás, María Teresa; García-Ordás, Diego; Fernández-Robles, Laura; Alegre, Enrique
Vitality assessment of boar sperm using n concentric squares resized and local binary pattern in gray scale images Artículo de revista
En: XXXIII Jornadas de Automatica, 2012.
Resumen | Enlaces | BibTeX | Etiquetas: Biomedical Image, local binary pattern, Sperm Vitality, texture classification
@article{garcia-olalla_vitality_2012,
title = {Vitality assessment of boar sperm using n concentric squares resized and local binary pattern in gray scale images},
author = {Óscar García-Olalla Olivera and María Teresa García-Ordás and Diego García-Ordás and Laura Fernández-Robles and Enrique Alegre},
url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/268519830_Vitality_assessment_of_boar_sperm_using_N_Concentric_Squares_resized_and_Local_binary_pattern_in_gray_scale_images/links/546f374b0cf24af340c07a96/Vitality-assessment-of-boar-sperm-using-N-Concentric-Squares-resized-and-Local-binary-pattern-in-gray-scale-images.pdf},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {XXXIII Jornadas de Automatica},
abstract = {This work introduces a new texture descriptor combining Local Binary Pattern (LBP) and N-concentric squares resized (NCSR) to classify boar spermatozoa as dead or alive using grayscale images. The classifier used was Support Vector Machine (SVM), and the best performance (78.67% hit rate) was achieved with NCSR of 50 features and LBP with 16 neighbors. This result outperforms previous methods in the field, making it a promising tool for automating sperm vitality classification in veterinary practices.},
keywords = {Biomedical Image, local binary pattern, Sperm Vitality, texture classification},
pubstate = {published},
tppubtype = {article}
}
This work introduces a new texture descriptor combining Local Binary Pattern (LBP) and N-concentric squares resized (NCSR) to classify boar spermatozoa as dead or alive using grayscale images. The classifier used was Support Vector Machine (SVM), and the best performance (78.67% hit rate) was achieved with NCSR of 50 features and LBP with 16 neighbors. This result outperforms previous methods in the field, making it a promising tool for automating sperm vitality classification in veterinary practices.
2011
3.
Alegre, Enrique; García-Ordás, María Teresa; González-Castro, Victor; Karthikeyan, S
Vitality assessment of boar sperm using N concentric squares resized (NCSR) texture descriptor in digital images Artículo de revista
En: Pattern Recognition and Image Analysis: 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8-10, 2011. Proceedings 5, pp. 540–547, 2011, (Publisher: Springer Berlin Heidelberg).
Resumen | Enlaces | BibTeX | Etiquetas: local binary pattern, Sperm Vitality, support vector machine, texture descriptors
@article{alegre_vitality_2011,
title = {Vitality assessment of boar sperm using N concentric squares resized (NCSR) texture descriptor in digital images},
author = {Enrique Alegre and María Teresa García-Ordás and Victor González-Castro and S Karthikeyan},
url = {https://link.springer.com/chapter/10.1007/978-3-642-21257-4_67},
year = {2011},
date = {2011-01-01},
journal = {Pattern Recognition and Image Analysis: 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8-10, 2011. Proceedings 5},
pages = {540–547},
abstract = {Two new textural descriptor, named N Concentric Squares Resized (NCSR) and N Concentric Squares Histogram (NCSH), have been proposed. These descriptors were used to classify 472 images of alive spermatozoa heads and 376 images of dead spermatozoa heads. The results obtained with these two novel descriptors have been compared with a number of classical descriptors such as Haralick, Pattern Spectrum, WSF, Zernike, Flusser and Hu. The feature vectors computed have been classified using kNN and a backpropagation Neural Network. The error rate obtained for NCSR with N = 11 was of 23.20% outperforms the rest of descriptors. Also, the area under the ROC curve (AUC) and the values observed in the ROC curve indicates the performance of the proposed descriptor is better than the others texture description methods.},
note = {Publisher: Springer Berlin Heidelberg},
keywords = {local binary pattern, Sperm Vitality, support vector machine, texture descriptors},
pubstate = {published},
tppubtype = {article}
}
Two new textural descriptor, named N Concentric Squares Resized (NCSR) and N Concentric Squares Histogram (NCSH), have been proposed. These descriptors were used to classify 472 images of alive spermatozoa heads and 376 images of dead spermatozoa heads. The results obtained with these two novel descriptors have been compared with a number of classical descriptors such as Haralick, Pattern Spectrum, WSF, Zernike, Flusser and Hu. The feature vectors computed have been classified using kNN and a backpropagation Neural Network. The error rate obtained for NCSR with N = 11 was of 23.20% outperforms the rest of descriptors. Also, the area under the ROC curve (AUC) and the values observed in the ROC curve indicates the performance of the proposed descriptor is better than the others texture description methods.