Optimizing the classification of biological tissues using machine learning models based on polarized data. Issue 4 (23rd December 2022) - Rodríguez, Carla
Abstract: Polarimetric data is nowadays used to build recognition models for the characterization of organic tissues or the early detection of some diseases. Different Mueller matrix‐derived polarimetric observables, which allow a physical interpretation of a specific characteristic of samples, are proposed in literature to feed the required recognition algorithms. However, they are obtained through mathematical transformations of the Mueller matrix and this process may loss relevant sample information in search of physical interpretation. In this work, we present a thorough comparative between 12 classification models based on different polarimetric datasets to find the ideal polarimetric framework to construct tissues classification models. The study is conducted on the experimental Mueller matrices images measured on different tissues: muscle, tendon, myotendinous junction and bone; from a collection of 165 ex‐vivo chicken thighs. Three polarimetric datasets are analyzed: (A) a selection of most representative metrics presented in literature; (B) Mueller matrix elements; and (C) the combination of (A) and (B) sets. Results highlight the importance of using raw Mueller matrix elements for the design of classification models. Abstract : This work presents a thorough comparative between three polarimetric datasets composed by (A) a selection of most representative metrics presented in literature; (B) Mueller matrix elements; and (C) the combination of (A) and (B); to find the ideal polarimetric framework to construct organic tissue classification models. The best proposed models achieve global accuracy scores larger than 97% and classification rates superior to 99% for tendon, myotendinous junction and bone when using (B) and (C) datasets.