Presentation + Paper
15 March 2023 Study of the suitability of polarimetric metrics to implement tissue classification models based on artificial intelligence
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Abstract
Polarimetric images are used for the characterization of biological tissues as well as for the early detection of some diseases. Recently, it has been demonstrated that accurate classification models can be constructed based on polarimetric data, such as the Mueller matrix (MM) or different polarimetric metrics resulting from combinations of different MM elements. The choice of polarimetric observables to be used for classifying is usually arbitrary, but mathematical transformations from MM elements to other metrics may benefit or impair the accuracy of the final models. This work presents a thorough comparison of different classification models based on typical machine learning algorithms trained according to different polarimetric metrics, in the search of the most efficient polarimetric basis. The classification models are tested on different biological tissues obtained from a collection of ex-vivo chickens.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Irene Estévez, Carla Rodríguez, Mónica Canabal, Emilio González-Arnay, Juan Campos, and Angel Lizana "Study of the suitability of polarimetric metrics to implement tissue classification models based on artificial intelligence", Proc. SPIE 12382, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023, 1238208 (15 March 2023); https://doi.org/10.1117/12.2666246
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KEYWORDS
Polarimetry

Tissues

Biological samples

Mueller matrices

Machine learning

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