Optimizing the classification of biological tissues using polarized data supported by machine learning

Irene Estévez; Mónica Canabal-Carbia; Carla Rodríguez; Juan Campos; Ángel Lizana

doi:10.1117/12.2673758

11 August 2023 Optimizing the classification of biological tissues using polarized data supported by machine learning

Irene Estévez, Mónica Canabal-Carbia, Carla Rodríguez, Juan Campos, Ángel Lizana

Proceedings Volume 12629, Emerging Technologies for Cell and Tissue Characterization II; 126290W (2023) https://doi.org/10.1117/12.2673758
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

Polarimetric data is nowadays used in the biomedical field to inspect organic tissues or for the early detection of some pathologies. In this work, we present a thorough comparison between different classification models based on several sets of polarimetric data, this allowing us to choose the polarimetric framework to construct tissue classification models. Four different well-known machine learning models are compared by analyzing three polarimetric datasets: (i) a selection of ten representative polarimetric observables; (ii) the Mueller matrix elements; and (iii) the combination of (i) and (ii) datasets. The study is conducted on the experimental Mueller matrices images measured on different organic tissues: muscle, tendon, myotendinous junction and bone; all of them measured from a collection of 165 ex-vivo chicken thighs. Provided results show the potential of polarimetric datasets for classification of biological tissues and paves the way for future applications in biomedicine and clinical trials.

Citation Download Citation

Irene Estévez, Mónica Canabal-Carbia, Carla Rodríguez, Juan Campos, and Ángel Lizana "Optimizing the classification of biological tissues using polarized data supported by machine learning", Proc. SPIE 12629, Emerging Technologies for Cell and Tissue Characterization II, 126290W (11 August 2023); https://doi.org/10.1117/12.2673758

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