Open AccessPolarimetric data-based model for tissue recognition
Author(s) -
Carla Rodríguez,
Albert Van Eeckhout,
Laia Ferrer,
Enric Garcia-Caurel,
Emilio GonzálezArnay,
Juan Campos,
Ángel Lizana
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.426387
Subject(s) - polarimetry , computer science , parametric statistics , sample (material) , artificial intelligence , pattern recognition (psychology) , biomedical engineering , optics , scattering , mathematics , statistics , physics , medicine , thermodynamics
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.