Depolarization metric spaces for biological tissues classification

Classification of tissues is an important problem in biomedicine. An efficient tissue classification protocol allows, for instance, the guided‐recognition of structures through treated images or discriminating between healthy and unhealthy regions (e.g., early detection of cancer). In this framework, we study the potential of some polarimetric metrics, the so‐called depolarization spaces, for the classification of biological tissues. The analysis is performed using 120 biological ex vivo samples of three different tissues types. Based on these data collection, we provide for the first time a comparison between these depolarization spaces, as well as with most commonly used depolarization metrics, in terms of biological samples discrimination. The results illustrate the way to determine the set of depolarization metrics which optimizes tissue classification efficiencies. In that sense, the results show the interest of the method which is general, and which can be applied to study multiple types of biological samples, including of course human tissues. The latter can be useful for instance, to improve and to boost applications related to optical biopsy. In this work we analyze the suitability of different depolarizing parameters to classify different biological tissues. The study focuses on classifying different tissues from a collection of ex vivo samples by using different depolarizing spaces. The tissues classification efficiencies obtained for some depolarization spaces studied demonstrate their potential for tissue classification tasks, especially when compared with other commonly used depolarizing metrics. The figure shows the 3D representation in the indices of polarimetric purity space of the experimental nonsymmetric ellipsoids obtained for ex vivo chicken thighs measured at 625 nm.