Publications

@article{figueroa_towards_2017,

title = {Towards the development of non-invasive measures of breast cancer risk: image analysis of digital breast tomosynthesis mammograms and tissue lobule content.},

author = {Jonine Figueroa and Calum Gray and Giorgos Papanastasiou and Víctor González-Castro and Nick Polydorides and Evans Andrew and Sarah Vinnicombe},

url = {https://repository.essex.ac.uk/28227/1/J.Figueroa,%20%5B%5D,%20Papanastasiou,%20et%20al%20EMIM%202017.pdf},

year  = {2017},

date = {2017-01-01},

abstract = {Towards the development of non-invasive measures of breast cancer risk: image analysis of digital breast tomosynthesis mammog. See discussions, stats, and author profiles for this publication.},

keywords = {Breast Cancer, Digital Breast Tomosynthesis, image analysis, Mammography, Risk Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez-robles_desarrollo_2015,

title = {DESARROLLO DE UNA APLICACIÓN DE RECUPERACIÓN DE OBJETOS Y ANÁLISIS DE LOS DESCRIPTORES LOCALES INVARIANTES UTILIZADOS},

author = {Laura Fernández-Robles and Javier Alfonso-Cendón and Manuel Castejón-Limas and Óscar García-Olalla Olivera and Enrique Alegre},

url = {http://dspace.aeipro.com/xmlui/handle/123456789/725},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

abstract = {This paper presents an application for the ASASEC project that helps retrieve objects from large image and video datasets related to child exploitation cases. By selecting regions of interest and using descriptors like SIFT and COSFIRE, the application finds similar objects across datasets. Experiments showed SIFT and COSFIRE outperformed SURF and HOG in retrieval accuracy.},

keywords = {image analysis, Object Retrieval, SIFT},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_olivera_adaptive_2013,

title = {Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification},

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

url = {https://link.springer.com/article/10.1186/1687-5281-2013-31},

year  = {2013},

date = {2013-01-01},

journal = {EURASIP journal on image and video processing},

volume = {2013},

pages = {1–11},

abstract = {This paper proposes a new texture description method combining local and global texture descriptors for image classification. The adaptive local binary pattern with oriented standard deviation (ALBPS) method provides enhanced local features, while the global description uses a wavelet transform-based descriptor, WCF13. These descriptors were combined with a support vector machine for classification, yielding high accuracy (85.63%) and F-score (0.886) for spermatozoa data and good results (84.45%) for the KTH-TIPS 2a dataset. The hybrid approach outperformed previous methods.},

note = {Publisher: Springer International Publishing},

keywords = {adaptive local binary pattern, hybrid feature extraction, image analysis, local binary pattern, spermatozoa assessment, support vector machine, texture classification, wavelet trasform},

pubstate = {published},

tppubtype = {article}

}

@article{barreiro_use_2008,

title = {Use of descriptors based on moments from digital images for tool wear monitoring},

author = {Joaquín Barreiro and Manuel Castejón-Limas and Enrique Alegre and LK Hernández},

url = {https://www.sciencedirect.com/science/article/pii/S0890695508000096},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {International Journal of Machine Tools and Manufacture},

volume = {48},

number = {9},

pages = {1005–1013},

abstract = {This paper addresses the overly conservative criteria for tool replacement, which leads to tools being replaced prematurely, thus increasing costs. The study explores using moments to describe tool wear images and classify the tool condition into different wear classes. Hu and Legendre descriptors were found to perform the best for wear identification. These descriptors were analyzed using a finite mixture model (mclust) to classify tools into three wear classes: low, medium, and high. Discriminant analysis techniques, including linear and quadratic methods, were used to validate the clustering results. The paper suggests that the new wear criterion, based on the probability of belonging to a wear class, can replace the traditional conservative approach, potentially reducing tool replacement costs.},

note = {Publisher: Pergamon},

keywords = {Cluster Analysis, cutting tool, image analysis, Wear},

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}

}