Abstract
Mueller matrix polarimetry is exploited to find a potential polarization feature sensitive to subwavelength pore size variation in porous alumina samples. After careful analysis using standard machine learning methods, it is observed that existing Mueller matrix decomposition methods and parameters are insufficient to distinguish areas with different pore sizes. Thus, a new angular-based Mueller matrix polarimetry parameter capable of linearly separating areas with varying pore sizes is proposed. Such an angular-based parameter is novel because it is based on angular parameters, it utilizes multi-angle measurements, and it extracts physical information independent of existing decomposition methods or parameters. Hopefully this work should inspire future research on the angular parameters in Mueller matrix polarimetry and their relationships to microstructure information.
Highlights
Mueller matrix (MM) microscopy is a promising tool for scientific research and clinical application because it reveals the intrinsic optical property of objects [2–4]
Scholars commonly start by analyzing the Mueller matrix parameters such as the Mueller matrix polar decomposition (MMPD) and Mueller matrix transformation (MMT), which are interpretable physical parameters in extremely simplified models [4, 12, 13]
A pore size discriminative parameter is proposed based on the Mueller matrix angular parameter with multi-angle measurement
Summary
Mueller matrix (MM) microscopy is a promising tool for scientific research and clinical application because it reveals the intrinsic optical property of objects [2–4]. Scholars commonly start by analyzing the Mueller matrix parameters such as the Mueller matrix polar decomposition (MMPD) and Mueller matrix transformation (MMT), which are interpretable physical parameters in extremely simplified models [4, 12, 13]. This approach can be effective but not sufficient because in almost all cases, the samples are too complex to be differentiated using these simple parameters. PBPs are linearly combined to create polarization feature parameters (PFPs), which are much more microstructure-specific. This approach is proven useful in pathological samples [4–6]. To differentiate more complex samples, nonlinear models in machine learning could be utilized, but such models are often uninterpretable, and the results are not generalizable
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