Comparison between image texture and polarization features in histopathology

Yudi Liu; Yang Dong; Lu Si; Ruoyu Meng; Yanmin Dong; Hui Ma

doi:10.1364/BOE.416382

Comparison between image texture and polarization features in histopathology

Biomed Opt Express. 2021 Feb 23;12(3):1593-1608. doi: 10.1364/BOE.416382. eCollection 2021 Mar 1.

Authors

Yudi Liu^{1

2}, Yang Dong^{1

2}, Lu Si¹, Ruoyu Meng³, Yanmin Dong⁴, Hui Ma^{1

3

5}

Affiliations

¹ Center for Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen 518055, China.
² These authors contributed equally to this work.
³ Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Graduate School at Shenzhen Tsinghua University, Shenzhen 518055, China.
⁴ Shenzhen Hospital of Traditional Chinese Medicine, Shenzhen 518034, China.
⁵ Department of Physics, Tsinghua University, Beijing 100084, China.

Abstract

Digital pathology has shown great importance for diagnostic purposes in the digital age by integrating basic image features into multi-modality information. We quantify the degree of correlation between the multiple texture features from H&E images and polarization parameter sets derived from Mueller matrix images of the same sample to provide more microstructural information for assisting diagnosis. The experimental result shows the correlations between texture feature and polarization parameter via Pearson coefficients. Polarization parameters t₁ , D_L and the depolarization parameter Δ correlated with image texture features Tamura_Fcon and Tamura_Frgh, and can be used as powerful tools to quantitatively characterize cell nuclei related with tumor progression in breast pathological tissues. Polarization parameters δ and r_L associated with the image texture feature Tamura_Flin have great potential for the quantitative characterization of proliferative fibers produced by inflammation. Furthermore, polarization parameters have the advantages of stable recognition in low resolution images. This work validates the associations between image texture features and polarization parameters and the merit of polarization imaging methods in low-resolution situations.