Mueller matrix describes the polarization properties of the samples comprehensively, characterizing microstructural information at subcellular-level. Mueller matrix microscopy is a promising non-invasive tool for pathological diagnosis, but it can be challenging to extract polarization parameters that correlate with pathological variation. In this study, we propose a polarization super-pixel based polarization feature extraction framework. Polarization super-pixels are able to represent the polarization features of the biological sample in a simple, compact, and comprehensive way, while reducing the data volume drastically. Using various pathological samples including breast cancer, liver cancer, and lung cancer, we show that polarization super-pixel approach greatly increases the efficiency and performance of the downstream supervised and unsupervised learning tasks, for cancerous tissue identification and microstructural composition analysis. We also propose the super-pixel based label spreading method, which iteratively propagates pathologist’s initial manual label of cancerous region to the entire field of view, highlighting the tissues with the same microstructural features.
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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