Publications

@article{gonzalez-castro_texture-based_2016,

title = {Texture-based classification for the automatic rating of the perivascular spaces in brain MRI},

author = {V́ıctor González-Castro and María del Carmen Valdés-Hernández and Paul A Armitage and Joanna M Wardlaw},

url = {https://www.sciencedirect.com/science/article/pii/S1877050916311802},

year  = {2016},

date = {2016-01-01},

journal = {Procedia Computer Science},

volume = {90},

pages = {9–14},

abstract = {This paper investigates the classification of enlarged perivascular spaces (PVS) in the basal ganglia (BG) using texture features extracted from structural brain MRI. The texture is described through first-order statistics, co-occurrence matrix features, discrete wavelet transform coefficients (WSF and WCF), and local binary patterns (LBP). The texture features are classified using a Support Vector Machine (SVM). Experimental results show that WCF provides an accuracy of 80.03% in classifying the density of enlarged PVS.},

note = {Publisher: Elsevier},

keywords = {Discrete Wavelet Transform, local binary pattern, perivascular spaces, support vector machine, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_texture_2015,

title = {Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions},

author = {Víctor González-Castro and Johan Debayle and Yanal Wazaefi and Mehdi Rahim and Caroline Gaudy-Marqueste and Jean-Jacques Grob and Bernard Fertil},

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-24/issue-6/061104/Texture-descriptors-based-on-adaptive-neighborhoods-for-classification-of-pigmented/10.1117/1.JEI.24.6.061104.short},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Electronic Imaging},

volume = {24},

number = {6},

pages = {061104–061104},

abstract = {This paper proposes two texture descriptors for the automatic classification of skin lesions from dermoscopic images, focusing on color texture analysis. The first descriptor uses adaptive mathematical morphology (MM) and Kohonen self-organizing maps (SOM), while the second uses local binary patterns (LBP) with adaptive neighborhoods. Neither approach requires prior segmentation. The results show that the adaptive neighborhood-based LBP approach outperforms both nonadaptive versions of the proposed descriptors and dermatologists' visual predictions, as confirmed by receiver operating characteristic analysis.},

note = {Publisher: Society of Photo-Optical Instrumentation Engineers},

keywords = {Adaptive Neighborhoods, Dermoscopic Images, LBP, Local Binary Patterns, skin lesion classification, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_curvelet-based_2012,

title = {Curvelet-based texture description to classify intact and damaged boar spermatozoa},

author = {Víctor González-Castro and Enrique Alegre and Oscar García-Olalla and Diego García-Ordás and María Teresa García-Ordás and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-642-31298-4_53},

year  = {2012},

date = {2012-01-01},

journal = {Image Analysis and Recognition: 9th International Conference, ICIAR 2012, Aveiro, Portugal, June 25-27, 2012. Proceedings, Part II 9},

pages = {448–455},

abstract = {This paper proposes a new method for assessing boar sperm head images based on texture descriptors derived from the Curvelet Transform, aiming to automate acrosome status detection. Compared to other methods using Wavelet Transform and moments-based descriptors, the Curvelet-based texture descriptors outperformed the others, achieving a 97% hit rate and an area under the ROC curve of 0.99.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {Acrosome Status, Automation in Veterinary Diagnostics, Boar Sperm Analysis, Curvelet Transform, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{robles_local_2011,

title = {A local invariant features approach for classifying acrosome integrity in boar spermatozoa},

author = {L Fernández Robles and V González-Castro and O Garcıa-Olalla and MT Garcıa-Ordás and E Alegre},

url = {https://books.google.es/books?hl=en&lr=&id=rr7LBQAAQBAJ&oi=fnd&pg=PA199&dq=info:qN1Kvkc9MngJ:scholar.google.com&ots=wusFRCDzZe&sig=uD2_yECMO1Ldc5iYS0gzYkLkGp8&redir_esc=y#v=onepage&q&f=false},

year  = {2011},

date = {2011-01-01},

journal = {Computational Vision and Medical Image Processing: VipIMAGE 2011},

pages = {199},

abstract = {In this work we have used a number of texture descriptors to characterize the acrosome state of boar sperm cells, which is a key factor in semen quality control applications. Laws masks, Legendre and Zernike moments, Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform, and descriptors based on interest points using the Speeded-Up Robust Features (SURF) method have been evaluated. Classification using kNN show that the best results were obtained by SURF, with an overall hit rate of 94.88% and, what is more important, a higher hit rate in the damaged (96.86%) than in the intact class (92.89%). These results make this descriptor very attractive for the veterinary community.},

note = {Publisher: CRC Press},

keywords = {acrosome state, sperm cell analysis, SURF method, texture descriptors, veterinary applications},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_boar_2011,

title = {Boar spermatozoa classification using longitudinal and transversal profiles (LTP) descriptor in digital images},

author = {Enrique Alegre and Oscar García-Olalla and Víctor González-Castro and Swapna Joshi},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21073-0_36},

year  = {2011},

date = {2011-01-01},

journal = {Combinatorial Image Analysis: 14th International Workshop, IWCIA 2011, Madrid, Spain, May 23-25, 2011. Proceedings 14},

pages = {410–419},

abstract = {A new textural descriptor called Longitudinal and Transversal Profiles (LTP) has been proposed to classify images of dead and alive spermatozoa heads. The dataset consists of 376 dead spermatozoa head images and 472 alive ones. The performance of LTP was compared to other descriptors like Pattern Spectrum, Flusser, Hu, and a histogram-based statistical descriptor. The feature vectors were classified using both a back-propagation Neural Network and the kNN algorithm. The LTP descriptor achieved a classification error of 30.58%, outperforming the other descriptors. Additionally, the Area Under the ROC Curve (AUC) confirmed that LTP provided better performance than the other texture descriptors.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {Image classification, kNN, LTP Descriptor, Neural Network, Spermatozoa Classification, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez_robles_local_2011,

title = {A local invariant features approach for classifying acrosome integrity in boar spermatozoa},

author = {Laura Fernández Robles and Víctor González Castro and O Garcıa Olalla and María Teresa Garcıa Ordás and Enrique Alegre},

url = {https://books.google.es/books?hl=en&lr=&id=rr7LBQAAQBAJ&oi=fnd&pg=PA199&dq=info:qN1Kvkc9MngJ:scholar.google.com&ots=wusFRCDzZe&sig=uD2_yECMO1Ldc5iYS0gzYkLkGp8&redir_esc=y#v=onepage&q&f=false},

year  = {2011},

date = {2011-01-01},

journal = {Computational Vision and Medical Image Processing: VipIMAGE 2011},

pages = {199},

abstract = {In this work we have used a number of texture descriptors to characterize the acrosome state of boar sperm cells, which is a key factor in semen quality control applications. Laws masks, Legendre and Zernike moments, Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform, and descriptors based on interest points using the Speeded-Up Robust Features (SURF) method have been evaluated. Classification using kNN show that the best results were obtained by SURF, with an overall hit rate of 94.88% and, what is more important, a higher hit rate in the damaged (96.86%) than in the intact class (92.89%). These results make this descriptor very attractive for the veterinary community.},

note = {Publisher: CRC Press},

keywords = {acrosome state, sperm cell analysis, SURF method, texture descriptors, veterinary applications},

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 and others},

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

year  = {2009},

date = {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{alegre_computer_2008,

title = {Computer vision and classification techniques on the surface finish control in machining processes},

author = {Enrique Alegre and Joaquín Barreiro and Manuel Castejón and Sir Suarez},

url = {https://link.springer.com/chapter/10.1007/978-3-540-69812-8_110},

year  = {2008},

date = {2008-01-01},

journal = {International Conference Image Analysis and Recognition},

pages = {1101–1110},

abstract = {This work presents a method for surface finish control using computer vision. The test parts were made of AISI 303 stainless steel and machined with a CNC lathe. Using a Pulnix camera, diffuse illumination, and industrial zoom, 140 images were captured. Three feature extraction methods were applied: histogram statistics, Haralick descriptors, and Laws descriptors. Using k-NN, the best hit rate achieved was 92.14% with unfiltered images using Laws features. These results demonstrate the feasibility of using texture descriptors to assess the roughness of metallic parts for quality inspection.},

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

keywords = {AISI 303, Computer vision, Product Quality Inspection, Surface Finish Control, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_design_2006,

title = {Design of a computer vision system to estimate tool wearing},

author = {Enrique Alegre and Joaquín Barreiro and H Cáceres and LK Hernández and RA Fernández and M Castejón},

url = {https://www.scientific.net/MSF.526.61},

year  = {2006},

date = {2006-01-01},

journal = {Materials Science Forum},

volume = {526},

pages = {61–66},

abstract = {This work presents an application developed in Matlab to assess the wear level of tool inserts in automated processes using artificial vision techniques. The system acquires images at different resolutions and processes them through pre-processing, segmentation, and post-processing stages. Initial results using various texture descriptors are presented, but further experiments with a larger dataset are needed for validation.},

note = {Publisher: Trans Tech Publications Ltd},

keywords = {Artificial Vision, Matlab, texture descriptors, Wear Level Assessment},

pubstate = {published},

tppubtype = {article}

}