Publications

@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{sanchez-gonzalez_statistical_2006,

title = {Statistical approach to boar semen evaluation using intracellular intensity distribution of head images},

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

url = {https://research.rug.nl/en/publications/statistical-approach-to-boar-semen-evaluation-using-intracellular},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

journal = {Cellular and molecular biology},

volume = {52},

number = {6},

pages = {38–43},

abstract = {This study presents a method to classify boar sperm heads by analyzing intracellular intensity distributions in microscopic images. After pre-processing the images, a model of intensity distribution for living cells is created. Deviations from this model are used to classify sperm as alive or dead. The method provides accurate estimations of live cell fractions, with an error margin of less than 8%, meeting veterinary requirements.},

keywords = {boar semen, Classification, Concentration of Alive Cells, image analysis, Intracellular Intensity Distribution, Morphometry, SPERM, Sperm Heads},

pubstate = {published},

tppubtype = {article}

}

@article{tejerina_video_2006,

title = {Video camera as source of variation of the motility parameters from computer analysis of boar semen},

author = {F Tejerina and R González and B Alegre and Enrique Alegre and Manuel Castejón-Limas and AJ Martín and S Cárdenas and JC García and F Abad and JC Domínguez},

url = {https://catoute.unileon.es/discovery/openurl?institution=34BUC_ULE&vid=34BUC_ULE:VU1&volume=41&date=2006&aulast=Tejerina&issue=4&issn=0936-6768&spage=325&auinit=F&title=Reproduction%20in%20domestic%20animals.&atitle=Video%20camera%20as%20source%20of%20variation%20of%20the%20motility%20parameters%20from%20computer%20analysis%20of%20boar%20semen&sid=google},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

journal = {REPRODUCTION IN DOMESTIC ANIMALS},

volume = {41},

number = {4},

pages = {325–325},

abstract = {Video camera as source of variation of the motility parameters from computer analysis of boar semen},

note = {Publisher: BLACKWELL PUBLISHING 9600 GARSINGTON RD, OXFORD OX4 2DQ, OXON, ENGLAND},

keywords = {boar semen, Computational Analysis, Sperm Motility},

pubstate = {published},

tppubtype = {article}

}

@article{robles_tracking_2004,

title = {Tracking algorithms evaluation in feature points image sequences},

author = {Vanessa Robles and Enrique Alegre and Jose M Sebastian},

url = {https://link.springer.com/chapter/10.1007/978-3-540-30126-4_72},

year  = {2004},

date = {2004-01-01},

journal = {Image Analysis and Recognition: International Conference, ICIAR 2004, Porto, Portugal, September 29-October 1, 2004, Proceedings, Part II 1},

pages = {589–596},

abstract = {In this work, different techniques of target tracking in video sequences have been studied. The aim is to decide whether the evaluated algorithms can be used to determine and analyze a special kind of trajectories. Different Feature Point Tracking Algorithms have been implemented. They solve the correspondence problem starting from a detected point set. After carrying out various experiments with synthetic and real points, we present an algorithm result assessment showing their adaptability in our problem: boar semen video sequences.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {boar semen, Feature Point Tracking, Target Tracking, Trajectory Analysis, Video Processing},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_adaptive_nodate,

title = {Adaptive texture description for semen vitality assessment},

author = {Víctor González-Castro and Enrique Alegre and Alexci Suárez-Castrillón and Óscar García-Olalla Olivera and María Teresa García-Ordás},

url = {https://d1wqtxts1xzle7.cloudfront.net/47501584/Adaptive_texture_description_for_semen_v20160725-8795-17ysl7b-libre.pdf?1469454941=&response-content-disposition=inline%3B+filename%3DAdaptive_texture_description_for_semen_v.pdf&Expires=1739193493&Signature=SPojfSyGRHX-PAT8i5lgMywJ52650XScp8t74YrAAbRnnZ0mPJbvVLYRGRVG3eNTcH0gHbWwIshfAz3ok9lb8gwRdrSSDSazEnRN5cYx8eBGm1rhAQ0WmI7Cwc0TaLMt1nk41LMRM4hROUQEF9P6YZShfswvVnj~e9IXqGrjksaOYlzuk7Y2zsIzg4jnkgk~w1gJeZ-gxUj~5mdtEESX7zDTdTrbyvs4Roiyoig8~jgMdtkCptWrABzqnovahSK9D-vjedWjo-EVaVue22Wa-k1z7hSSerV1X~AZt68BQqa2GqhwOxQ~qfJdpU4j8o88~hi6RcbTtsGj7-BE9vyWZA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},

abstract = {In this paper an adaptive texture description method based on granulometry 

is proposed. It is a variant of the Pattern Spectrum, and both the adaptive 

and the ordinary descriptors are used to classify boar spermatozoa into 

dead or alive. A set of 845 boar spermatozoon heads were assessed and a 

back-propagation neural network was used in order to classify them. We 

have described both the original grey level images and the same images 

after applying a range texture filter on them. The best hit rates have been 

obtained when the adaptive Pattern Spectrum was used to describe the 

filtered images.},

keywords = {boar semen, granulometry, pattern spectrum, texture description},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_evaluation_nodate,

title = {Evaluation of mother wavelet functions with statistical texture descriptors to classify boar sperm acrosomes},

author = {Enrique Alegre and Maribel González and Víctor González-Castro and Tomás Alonso and María Teresa García-Ordás and Óscar García-Olalla Olivera},

url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/268519913_Evaluation_of_mother_wavelet_functions_with_statistical_texture_descriptors_to_classify_boar_sperm_acrosomes/links/546f3ce10cf2d67fc031030e/Evaluation-of-mother-wavelet-functions-with-statistical-texture-descriptors-to-classify-boar-sperm-acrosomes.pdf},

abstract = {This study utilizes a backpropagation neural network to determine the acrosome state of boar spermatozoa, evaluating the impact of various wavelet families on the classification accuracy. Five wavelet families—Dauchebies, Coiflets, Symlets, Meyer, and biorthogonal—were applied to the images, from which first and second-order texture descriptors were extracted. The classification process used a neural network, and different configurations were assessed. Results show a 7% variation in hit rates, with the best performance achieved using the biorthogonal and Symlets wavelet families, reaching a 95% success rate.},

keywords = {boar semen, texture description, wavelet transform},

pubstate = {published},

tppubtype = {article}

}