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.

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