Evaluating Tissue Mechanical Properties Using Quantitative Mueller Matrix Polarimetry and Neural Network

Abstract

Evaluation of the mechanical properties of biological tissues has always been an important issue in the field of biomedicine. The traditional method for mechanical properties measurement is to perform in vitro tissue deformation experiments. With the fast development of optical and image processing techniques, more and more non-invasive and non-contact optical methods have been applied to the analysis of tissue mechanical features. In this study, we use Mueller matrix polarimetry to quantitatively obtain the mechanical properties of bovine tendon tissues. Firstly, to study the structural information and the changes in the optical characteristics of the tendon tissue under different stretching states, 3 × 3 Mueller matrix images of bovine tendon tissue samples are acquired by backscattering measurement setups based on a polarized camera. Then, we extract the frequency distribution histograms (FDHs) of the Mueller matrix elements to reveal the structural changes of the tendon tissue more clearly during the stretching process. Last, we calculate the Mueller matrix transformation (MMT) parameters, the total anisotropy t1 and the anisotropy direction α1 of the tendon tissue samples under different stretching processes to quantitatively characterize their structural changes under different mechanical states. The central moments of the MMT parameters can be used to distinguish the different stretching states of the tendon tissue. For better discrimination based on the MMT parameters, we design a multilayer neural network that takes the first-order moments of the MMT parameters as the input features. After training, a high-precision classification model of the stretching states of tendon tissue samples is finally obtained, and the total classification accuracy achieves 98%. The experimental results show that the Mueller matrix polarimetry can be a potential non-contact tool for tissue mechanical properties evaluation.