Comparison between image texture and polarization features in histopathology

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, DL 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 rL 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.

Analyzing the Influence of Imaging Resolution on Polarization Properties of Scattering Media Obtained From Mueller Matrix

“…Additionally, we also choose the gray scale co-occurrence matrix (GLCM) method ( Haralick et al, 1973 ), which has been demonstrated as a powerful tool for the Mueller matrix imaging results analysis in recent studies ( Shen et al, 2020 ; Liu et al, 2021 ; Zhai et al, 2022 ), for the MMPD parameters comparisons. Here, the GLCM parameters Contrast, Correlation, Energy, and Homogeneity are adopted as shown in Eqs 8 – 12 .…”

“…When applied to clinical detection, the imaging speed should be considered in addition to the impact of imaging resolution. Some recent researches have shown that the polarization imaging method can better preserve the microstructural information of the sample when imaging resolution decreased compared to unpolarized optical imaging methods ( Shen et al, 2020 ; Liu et al, 2021 ; Chen et al, 2022 ; Yao et al, 2022 ). It indicates that adopting the polarization imaging method can well balance the requirements of imaging resolution and FOV, to acquire the micro- and even nanostructural ( Dong et al, 2020 ; Chen et al, 2021a ; Wang et al, 2021 ; Fang et al, 2022 ) properties of the scattering medium quickly using a relatively low NA objective.…”

Polarimetric biomarkers of peri-tumoral stroma can correlate with 5-year survival in patients with left-sided colorectal cancer

“…However, these properties may not directly highlight the underlying structure and arrangement of collagen within the tissue sample, and thus much work has gone into interpreting and correlating the observed polarimetric parameter changes with their core biological or biophysical meaning. Yet with the advancement of artificial intelligence (AI) in recent years, an alternative to this detailed understanding/interpretation has emerged whereby researchers have used various machine and deep learning techniques to directly link up the rich biological information from the polarization properties with clinical diagnosis and prognosis 15 , 27 – 33 .…”

Characterization of fiber content in biological tissues based on Stokes polarization imaging during dehydration process

“…Polarization measurement is widely used in the field of biomedicine due to its sensitivity to the microstructural changes of biological tissues and its noninvasive features. At present, polarization images have shown potential in the diagnosis of breast cancer [1,2], cervical cancer [3], chronic liver disease [4] and other diseases. Mueller matrix system is a typical polarization measurement method as the information dimension of Mueller matrix is high.…”