Publications

@article{porto-alvarez_ct-based_2023,

title = {CT-based radiomics to predict KRAS mutation in CRC patients using a machine learning algorithm: a retrospective study},

author = {Jacobo Porto-Álvarez and Eva Cernadas and Rebeca Aldaz-Martínez and Manuel Fernández-Delgado and Emilio Huelga and Víctor González-Castro and Sandra Baleato-González and Roberto García-Figueiras and José Ramón Antúnez-López and Miguel Souto-Bayarri},

url = {https://www.mdpi.com/2227-9059/11/8/2144},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Biomedicines},

volume = {11},

number = {8},

pages = {2144},

abstract = {This study examines the use of CT-based radiomics to predict KRAS mutations in colorectal cancer (CRC) patients. Several classifiers were tested, with AdaBoost on clinical data achieving the highest accuracy (76.8%). Texture descriptors also showed a correlation with KRAS mutations. Radiomics could reduce the need for invasive diagnostic methods for CRC in the future.},

note = {Publisher: MDPI},

keywords = {colorectal cancer, KRAS Mutation, Radiogenomics, Radiomics, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{ortiz-ramon_identification_2019,

title = {Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images},

author = {Rafael Ortiz-Ramón and Maria del Carmen Valdés-Hernández and Victor González-Castro and Stephen Makin and Paul A Armitage and Benjamin S Aribisala and Mark E Bastin and Ian J Deary and Joanna M Wardlaw and David Moratal},

url = {https://www.sciencedirect.com/science/article/pii/S0895611119300278},

year  = {2019},

date = {2019-01-01},

journal = {Computerized Medical Imaging and Graphics},

volume = {74},

pages = {12–24},

abstract = {This study investigates using radiomics to detect stroke lesions in brain MRI scans, which are often missed by automated methods. Analyzing 1800 MRI scans, the research found that radiomic features could identify stroke lesions with accuracy between 0.7 and 0.83 AUC. Age was the clinical factor most correlated with accurate detection. The study suggests that incorporating texture features into deep learning models could improve stroke lesion detection in MRI scans.},

note = {Publisher: Pergamon},

keywords = {Radiomics, small vessel disease, Stroke, texture analysis, white matter hyperintensities},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_automatic_2015,

title = {Automatic classification of skin lesions using color mathematical morphology-based texture descriptors},

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/conference-proceedings-of-spie/9534/953409/Automatic-classification-of-skin-lesions-using-color-mathematical-morphology-based/10.1117/12.2182592.short},

year  = {2015},

date = {2015-01-01},

journal = {Twelfth International Conference on Quality Control by Artificial Vision 2015},

volume = {9534},

pages = {53–59},

abstract = {This paper presents an automatic method for classifying skin lesions in dermoscopic images using color texture analysis. It combines mathematical morphology for local pixel descriptors with Kohonen Self-Organizing Maps (SOM) for clustering and global texture description, eliminating the need for segmentation. Two approaches—classical and adaptive morphology—achieve similar AUC scores (0.854 and 0.859), surpassing dermatologist predictions (0.792).},

note = {Publisher: SPIE},

keywords = {dermoscopic imaging, machine learning, skin lesion classification, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_new_2012,

title = {A new improved Laws-based descriptor for surface roughness evaluation},

author = {Enrique Alegre and Joaquín Barreiro and Alexci Suárez-Castrillón},

url = {https://link.springer.com/article/10.1007/s00170-011-3507-z},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {59},

pages = {605–615},

abstract = {A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes.},

note = {Publisher: Springer-Verlag},

keywords = {Computer vision, machining processes, surface roughness, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{morala-arguello_evaluation_2012,

title = {A evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain},

author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre},

url = {https://link.springer.com/article/10.1007/s00170-011-3480-6},

year  = {2012},

date = {2012-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {59},

pages = {213–220},

abstract = {This study proposes a multiresolution method for unmanned visual quality inspection and surface roughness discrimination in turning. Using wavelet transform, texture features were extracted from surface images, focusing on gray levels in vertical detail sub-images. A multilayer Perceptron neural network classified textures, achieving error rates between 2.59% and 4.17%.},

note = {Publisher: Springer-Verlag},

keywords = {neural networks, quality inspection, surface roughness, texture analysis, wavelet transform},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_tool_nodate,

title = {Tool wear classification using texture descriptors based on Local Binary Pattern},

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

url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=opCbArQAAAAJ:RHpTSmoSYBkC},

abstract = {This paper presents a new approach for tool wear identification in metal milling using texture descriptors LBP and ALBP, enhanced with LOSIB (Local Oriented Statistical Information Booster). Two datasets are considered: Cutting Edges (gray-scale images of worn cutting edges) and Edge Wear (cropped worn areas). Both datasets are labeled into two (low/high wear) and three (low/medium/high wear) classes. Classification using Support Vector Machine with Least Squares training achieved a 74.05% accuracy for two classes and 53.77% for three classes in the Edge Wear dataset.},

keywords = {Machine Learing, Manufacturing, SVM, texture analysis, Tool wear},

pubstate = {published},

tppubtype = {article}

}