Polarimetric data-based model for tissue recognition

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.

Automated summary

The potential of a predictive optical model method for tissue recognition based on statistical analysis of polarimetric indicators was highlighted. By analyzing experimental Mueller matrices of four biological tissues from 157 ex-vivo chicken samples, a logistic regression-based algorithm was developed to identify which tissue category a sample belongs to in a single dynamic measurement.