3 March 2017 Characterizing microstructural features of biomedical samples by statistical analysis of Mueller matrix images
Author Affiliations +
Abstract
As one of the salient features of light, polarization contains abundant structural and optical information of media. Recently, as a comprehensive description of polarization property, the Mueller matrix polarimetry has been applied to various biomedical studies such as cancerous tissues detections. In previous works, it has been found that the structural information encoded in the 2D Mueller matrix images can be presented by other transformed parameters with more explicit relationship to certain microstructural features. In this paper, we present a statistical analyzing method to transform the 2D Mueller matrix images into frequency distribution histograms (FDHs) and their central moments to reveal the dominant structural features of samples quantitatively. The experimental results of porcine heart, intestine, stomach, and liver tissues demonstrate that the transformation parameters and central moments based on the statistical analysis of Mueller matrix elements have simple relationships to the dominant microstructural properties of biomedical samples, including the density and orientation of fibrous structures, the depolarization power, diattenuation and absorption abilities. It is shown in this paper that the statistical analysis of 2D images of Mueller matrix elements may provide quantitative or semi-quantitative criteria for biomedical diagnosis.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Honghui He, Yang Dong, Jialing Zhou, Hui Ma, "Characterizing microstructural features of biomedical samples by statistical analysis of Mueller matrix images", Proc. SPIE 10063, Dynamics and Fluctuations in Biomedical Photonics XIV, 100630H (3 March 2017); doi: 10.1117/12.2251299; https://doi.org/10.1117/12.2251299
PROCEEDINGS
7 PAGES + PRESENTATION

SHARE
Back to Top