Ms. Yue Yao Profile

Yue Yao

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Proceedings Article | 4 March 2022 Presentation + Paper

Deep learning-based polarization feature retrieval from a single Stokes vector

Lu Si, Tongyu Huang, Xingjian Wang, Yue Yao, Hui Ma

Proceedings Volume 11963, 1196307 (2022) https://doi.org/10.1117/12.2609583

KEYWORDS: Polarization, Polarimetry, Tissues, Liver, Blood, Microscopes, Feature extraction, Polarizers

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Polarization is capable of probing microstructures and has unique sensitivity to fibrous anisotropic structure. Polarimetric imaging has demonstrated promising potential in diverse applications ranging from biomedicine, material science, and atmospheric remote sensing. The polarization properties of samples can be comprehensively described by a Mueller matrix (MM). However, the relationship between individual MM elements and properties of the sample is often not clear. There have been consistent efforts to derive polarization parameters from MM based on certain assumptions for better description of the samples, e.g., MM polar decomposition (MMPD), MM transformation (MMT) and MM differential decomposition. Usually, the MM imaging requires sequential measurements with different polarization states of incident light and the imaging process is time consuming. In addition, for movable samples, we cannot guarantee the consistency during the imaging. This may cause precision issues since the images cannot be well-registered. In this work, we built a statistical translation model to generate polarization parameters from a single Stokes vector which can be obtained by one-shot imaging. This will improve the imaging efficiency, simplify the optical system and avoid introducing errors by the image registration. In the model design, we adopted the generative adversarial network (GAN) where the generator is based on a U-net architecture. We demonstrated the effectiveness of our approach on liver tissue, blood smear and porous anodic alumina (PAA) film, and quantitatively evaluated the results by similarity assessment methods. The model can generate a parameter image within 0.1 second on a desktop computer, which shows the potential to achieve real-time performance.

Proceedings Article | 5 March 2021 Presentation + Paper

Mueller matrix orientation parameters unwrapping for statistically viable distribution

Jiachen Wan, Yue Yao, Yudi Liu, Stephen Arnold, Hui Ma

Proceedings Volume 11646, 116460L (2021) https://doi.org/10.1117/12.2577589

KEYWORDS: Statistical analysis, Spatial resolution, Quantitative analysis, Polarimetry, Microscopy, Imaging systems, Data hiding, Anisotropy

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Mueller matrix microscopy is a promising non-invasive tool for pathological diagnosis due to its sensitivity to microstructures and its non-reliance on high spatial resolution. Such technique is sensitive to anisotropy, but the majority of such information is deeply hidden within the orientation parameters such as αq, αr, αP and αD. Analysis of them is challenging because orientation parameters varies when the sample’s spatial azimuthal angle changes relative to the imaging system, and the range boundary imposed by the arctan function prevents the parameters from forming a continuous distribution. As the result, the use of orientation parameters is generally avoided during quantitative analysis, despite the rich information they encode. In an effort to resolve these challenges, we propose a novel method for analyzing orientation parameters extracted from Mueller matrix polarimetry. The angular pixel values in the parameter images are unwrapped by assuming continuity, transforming the distorted distribution into one that is statistically viable. The unwrapped orientation parameters are then used for pathological slides analysis. Frequency distribution histograms of the orientation parameters before and after unwrapping are compared, the validity of the proposed method is demonstrated.

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