Publications

@article{gonzalez-castro_reliability_2017,

title = {Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance},

author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Francesca M Chappell and Paul A Armitage and Stephen Makin and Joanna M Wardlaw},

url = {https://portlandpress.com/clinsci/article/131/13/1465/71656/Reliability-of-an-automatic-classifier-for-brain},

year  = {2017},

date = {2017-01-01},

journal = {Clinical Science},

volume = {131},

number = {13},

pages = {1465–1481},

abstract = {Enlarged perivascular spaces (PVS) in the brain are associated with small vessel disease, poor cognition, and hypertension. This study proposes a fully automated method using a support vector machine (SVM) to classify PVS burden in the basal ganglia (BG) as low or high from T2-weighted MRI images. Three feature extraction techniques were evaluated, with the bag of visual words (BoW) approach achieving the highest accuracy (81.16%). The classifier's performance was comparable to that of trained human observers, and its predictions were clinically meaningful, as indicated by high AUC values (0.90–0.93). These findings suggest that automated PVS burden assessment could serve as a valuable clinical tool.},

note = {Publisher: Portland Press Ltd.},

keywords = {Bag of Visual Words, brain MRI, Discrete Wavelet Transform, Local Binary Patterns, machine learning, perivascular spaces, small vessel disease, support vector machine},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_texture-based_2016,

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

author = {Víctor 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{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}

}