Publications

@article{mazo_automatic_2016,

title = {Automatic recognition of fundamental tissues on histology images of the human cardiovascular system},

author = {Claudia Mazo and María Trujillo and Enrique Alegre and Liliana Salazar},

url = {https://www.sciencedirect.com/science/article/pii/S0968432816301573},

year  = {2016},

date = {2016-01-01},

journal = {Micron},

volume = {89},

pages = {1–8},

abstract = {This paper presents an automatic method for classifying fundamental tissues in histological images using k-means clustering. It identifies epithelial, connective, and muscle tissues with high sensitivity (0.79–0.91) and expert validation (4.82–4.85/5), supporting medical diagnosis and education.},

note = {Publisher: Pergamon},

keywords = {histology, machine learning, medical imaging, tissue classification},

pubstate = {published},

tppubtype = {article}

}

@article{ballerini_application_2016,

title = {Application of the ordered logit model to optimising Frangi filter parameters for segmentation of perivascular spaces},

author = {Lucía Ballerini and Ruggiero Lovreglio and María del Carmen Valdés-Hernández and Víctor González-Castro and Susana Muñoz-Maniega and Enrico Pellegrini and Mark E Bastin and Ian J Deary and Joanna M Wardlaw},

url = {https://www.sciencedirect.com/science/article/pii/S1877050916311899},

year  = {2016},

date = {2016-01-01},

journal = {Procedia Computer Science},

volume = {90},

pages = {61–67},

abstract = {Segmenting perivascular spaces (PVS) in brain MRI is crucial for studying the brain's lymphatic system and its link to neurological diseases. The Frangi filter is a useful tool for this task, but its parameters must be optimized for different scanner settings. This study employs an ordered logit model to refine these parameters based on neuroradiological PVS ratings. The resulting PVS volume strongly correlates with expert assessments (Spearman’s ρ=0.75, p < 0.001), indicating that this approach is a promising alternative to conventional optimization methods.},

note = {Publisher: Elsevier},

keywords = {brain MRI, frangi filter, medical imaging, neuroimaging, perivascular spaces},

pubstate = {published},

tppubtype = {article}

}