Deformation Measurement by Two-Dimensional Multi-Camera Full-Field Digital Image Correlation

Cong, 刘聪 Liu; Yuntong, 戴云彤 Dai; Meiling, 戴美玲 Dai; Xiaopeng, 刘晓鹏 Liu; Chengpeng, 朱程鹏 Zhu; Xinxing, 邵新星 Shao; Xiaoyuan, 何小元 He

doi:10.3788/aos201636.1212002

Cited by 3 publications

(2 citation statements)

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“…Biological tissues have significant differences in morphology, structure, and function [1,2]. Assessment of the tissue mechanical properties is a crucial concern in the field of biomedical research, as these properties are closely related to many characteristics of organisms [3].…”

Section: Introductionmentioning

confidence: 99%

Evaluating tissue mechanical properties using Mueller matrix polarimetry

Fan

2023

Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023)

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Evaluating tissue mechanical properties is an important issue in the biomedical field. While traditional in vitro tissue deformation experiments have been used to measure mechanical properties, optical methods are becoming increasingly popular due to their non-invasive and non-contact advantages. In this study, we utilized Mueller matrix polarimetry to quantify the mechanical properties of bovine tendon tissue. We acquired 3×3 Mueller matrix images of the tendon tissue samples under various stretching states using a backscattering measurement setup based on a polarization camera, enabling us to examine changes in both structural information and optical properties. Subsequently, we extracted frequency distribution histograms of Mueller matrix elements to elucidate the structural changes in the tendon tissue during the stretching process. We then calculated the Mueller matrix transformation parameters, namely the total anisotropy t1 and anisotropy direction α1 of the tendon tissue samples under different stretching processes, to characterize their structural changes quantitatively. For better discrimination of tendon tissues under different stretching states, we trained an image classification neural network using the derived MMT parameters as input. Ultimately, we obtained a highly accurate model with 90% precision. The results demonstrate the potential of Mueller matrix polarimetry as a tool for evaluating tissue mechanical properties.

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Section: Introductionmentioning

confidence: 99%

Evaluating tissue mechanical properties using Mueller matrix polarimetry

Fan

2023

Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023)

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“…Evaluating the mechanical properties of biological tissues has always been an important issue in the field of biomedicine. Biological tissues vary widely in morphology, structure, and function and have characteristics such as heterogeneity, anisotropy, and viscoelasticity [1,2]. In addition, the morphology, structure, production and development, lesions, and even death of organisms are related to mechanical properties [3].…”

Section: Introductionmentioning

confidence: 99%

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

Shao

et al. 2022

Applied Sciences

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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 and the anisotropy direction 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.

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Multi-camera based full-field 3D displacement measurement using digital image correlation

Jiang

et al. 2020

2020 13th International Symposium on Computational Intelligence and Design (ISCID)

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Deformation Measurement by Two-Dimensional Multi-Camera Full-Field Digital Image Correlation

Cited by 3 publications

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Evaluating tissue mechanical properties using Mueller matrix polarimetry

Evaluating tissue mechanical properties using Mueller matrix polarimetry

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

Multi-camera based full-field 3D displacement measurement using digital image correlation

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