Publications

@article{mazo_ontology-based_2018,

title = {Ontology-based automatic reclassification of tissues and organs in histological images},

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

url = {https://ceur-ws.org/Vol-2100/paper9.pdf},

year  = {2018},

date = {2018-01-01},

journal = {Proceedings of the 12th Alberto Mendelzon International Workshop on Foundations of},

volume = {390},

pages = {1–4},

abstract = {Heterogeneous data sources, such as images and human knowledge, require different processing approaches. This paper integrates visual descriptors and an ontology to classify tissues and organs of the human cardiovascular system. A cascade Support Vector Machine (SVM) first classifies images based on texture descriptors, and then results are refined using a histological ontology. This combined approach improves classification accuracy compared to image-based methods alone.},

keywords = {Automatic Classification, histological ontology, Histology Images, image processing},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_classification_2017,

title = {Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM},

author = {Claudia Mazo and Enrique Alegre and Maria Trujillo},

url = {https://www.sciencedirect.com/science/article/pii/S0169260716305910},

year  = {2017},

date = {2017-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {147},

pages = {1–10},

abstract = {Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies.},

note = {Publisher: Elsevier},

keywords = {Automatic Classification, Fundamental Tissues, Histology Images, image processing, Organs of the Cardiovascular System},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_tissues_2017,

title = {Tissues classification of the cardiovascular system using texture descriptors},

author = {Claudia Mazo and Enrique Alegre and Maria Trujillo and Víctor González-Castro},

url = {https://link.springer.com/chapter/10.1007/978-3-319-60964-5_11},

year  = {2017},

date = {2017-01-01},

journal = {Annual Conference on Medical Image Understanding and Analysis},

pages = {123–132},

abstract = {This paper presents an automated classification approach for cardiovascular tissues using texture analysis. Rotation-invariant Local Binary Patterns (LBPri) and Haralick features were evaluated as descriptors, while Random Forest (RF) and Linear Discriminant Analysis (LDA) were tested for classification. The study categorized tissues into four classes, achieving high AUC values (up to 0.9994) using LBPri and RF, demonstrating the effectiveness of this method for tissue identification.},

note = {Publisher: Springer International Publishing Cham},

keywords = {Automatic Classification, Fundamental Tissues, Histology Images, image processing, Organs of the Cardiovascular System},

pubstate = {published},

tppubtype = {article}

}