Organic tissue recognition through polarimetric-based algorithm

Carla Rodríguez; Albert Van Eeckhout; Irene Estévez; Laia Ferrer; Enrique Garcia-Caurel; Emilio González-Arnay; Juan Campos; Angel Lizana

doi:10.1117/12.2606371

4 March 2022 Organic tissue recognition through polarimetric-based algorithm

Carla Rodríguez, Albert Van Eeckhout, Irene Estévez, Laia Ferrer, Enrique Garcia-Caurel, Emilio González-Arnay, Juan Campos, Angel Lizana

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Proceedings Volume 11963, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2022; 1196305 (2022) https://doi.org/10.1117/12.2606371
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

During the last decades, the attention on the application of polarimetric methods for biological tissues inspection has been increasing. Nowadays, organic tissue recognition algorithms are of potential interest in different research areas, as for instance, in biomedical applications for the early detection of diseases or the classification of biological structures. Based on the modifications in polarization that light-matter interactions produce, an exhaustive polarimetric analysis of the sample (extraction of dichroism, retardance and depolarization) may unveil the different tissue inherent characteristics and provide a complete description of how the biological structures interact with incident polarized light. By taking advantage of such polarimetric methods tissues characterization, we propose four predictive models corresponding to the recognition of four ex-vivo chicken tissue categories: bone, muscle, tendon and myotendinous junction tissue samples. The implemented multivariant probabilistic models are based on the logistic regression fit of the experimental Mueller matrixderived polarimetric observables (measured at three different wavelengths: 625 nm, 530 nm and 470nm): polarizance P, diattenuation D, depolarization content (Indices of Polarimetric Purity P1, P2, P3 and depolarization index 𝑃Δ), retardance (global, R, and linear δ) and optical rotation Ψ. As a result, we achieve stable predictive models whose output, in terms of sensitivity and specificity indicators, are of 82.6% and 80.6% for bone recognition, 85% and 93.5% for tendon, 86% and 88.8% for muscle and 82% and 71% for myotendinous junction, respectively. Obtained results suggest that these noninvasive methods could be applied in multiple biomedical scenarios such as for early diagnosis of pathologies.

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Carla Rodríguez, Albert Van Eeckhout, Irene Estévez, Laia Ferrer, Enrique Garcia-Caurel, Emilio González-Arnay, Juan Campos, and Angel Lizana "Organic tissue recognition through polarimetric-based algorithm", Proc. SPIE 11963, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2022, 1196305 (4 March 2022); https://doi.org/10.1117/12.2606371

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KEYWORDS

Tissues

Polarimetry

Bone

Inspection

Statistical modeling

Detection and tracking algorithms

Statistical analysis

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