Quantitatively differentiating microstructures of tissues by frequency distributions of Mueller matrix images.
We present a new way to extract characteristic features of the Mueller matrix images based on their frequency distributions and the central moments. We take the backscattering Mueller matrices of tissues with distinctive microstructures, and then analyze the frequency distribution histograms (FDHs) of all the matrix elements. For anisotropic skeletal muscle and isotropic liver tissues, we find that the shapes of the FDHs and their central moment parameters, i.e., variance, skewness, and kurtosis, are not sensitive to the sample orientation. Comparisons among different tissues further indicate that the frequency distributions of Mueller matrix elements and their corresponding central moments can be used as indicators for the characteristic microstructural features of tissues. A preliminary application to human cervical cancerous tissues shows that the distribution curves and central moment parameters may have the potential to give quantitative criteria for cancerous tissues detections.
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, ChinabTsinghua University, Department of Biomedical Engineering, Beijing 100084.
Chao
C
He
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, China.
Honghui
H
He
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, ChinacTsinghua University, Department of Physics, Beijing 100084, China.
Xianpeng
X
Li
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, ChinacTsinghua University, Department of Physics, Beijing 100084, China.
Jintao
J
Chang
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, ChinacTsinghua University, Department of Physics, Beijing 100084, China.
Ye
Y
Wang
Shenzhen Sixth People's Hospital (Nanshan Hospital) Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China.
Shaoxiong
S
Liu
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, China.
Nan
N
Zeng
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, China.
Yonghong
Y
He
Tsinghua University, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Shenzhen 518055, ChinacTsinghua University, Department of Physics, Beijing 100084, China.
Hui
H
Ma
D000465:Algorithms; D000818:Animals; D002417:Cattle; D002645:Chickens; D003937:Diagnosis, Differential; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008027:Light; D008859:Microscopy, Polarization / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D012031:Refractometry / Q000379:methods*; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D013552:Swine; D002583:Uterine Cervical Neoplasms / Q000473:pathology*