Polarimetric data-based model for tissue recognition
Carla Rodríguez, Albert Van Eeckhout, Laia Ferrer, Enric Garcia‐Caurel, Emilio González‐Arnay, Juan Campos, Ángel Lizana
Abstract
We highlight the potential of a predictive optical model method for tissue recognition, based on the statistical analysis of different polarimetric indicators that retrieve complete polarimetric information (selective absorption, retardance and depolarization) of samples. The study is conducted on the experimental Mueller matrices of four biological tissues (bone, tendon, muscle and myotendinous junction) measured from a collection of 157 ex-vivo chicken samples. Moreover, we perform several non-parametric data distribution analyses to build a logistic regression-based algorithm capable to recognize, in a single and dynamic measurement, whether a sample corresponds (or not) to one of the four different tissue categories.