Subwavelength metrological chracterization by Mueller matrix polarimeter and finite difference time domain method

Adhikari, Achyut; Dev, Kapil; Asundi, Anand

doi:10.1016/j.optlaseng.2016.06.014

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…Therefore, prostate tumor diagnosis, for each of the statistical moments

the sensitivity

specificity

and balanced accuracy

were calculated [ 56 , 57 , 58 , 59 ].…”

Section: Diagnostic Methodsmentioning

confidence: 99%

Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films

Wójcik,

Hu,

Ushenko

et al. 2024

Sensors

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Our work uses a polarization matrix formalism to analyze and algorithmically represent optical anisotropy by open dehydration of blood plasma films. Analytical relations for Jones matrix reconstruction of optical birefringence maps of protein crystal networks of dehydrated biofluid films are found. A technique for 3D step-by-step measurement of the distributions of the elements of the Jones matrix or Jones matrix images (JMI) of the optically birefringent structure of blood plasma films (BPF) has been created. Correlation between JMI maps and corresponding birefringence images of dehydrated BPF and saliva films (SF) obtained from donors and prostate cancer patients was determined. Within the framework of statistical analysis of layer-by-layer optical birefringence maps, the parameters most sensitive to pathological changes in the structure of dehydrated films were found to be the central statistical moments of the 1st to 4th orders. We physically substantiated and experimentally determined the sensitivity of the method of 3D polarization scanning technique of BPF and SF preparations in the diagnosis of endometriosis of uterine tissue.

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“…Therefore, prostate tumor diagnosis, for each of the statistical moments

the sensitivity

specificity

and balanced accuracy

were calculated [ 56 , 57 , 58 , 59 ].…”

Section: Diagnostic Methodsmentioning

confidence: 99%

Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films

Wójcik,

Hu,

Ushenko

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…For instance, there are various matrix properties and quantities one can derive to test the physicality of the matrix. [102][103][104][105] Noise and systematic errors might give over-polarizing or unphysical Mueller matrices. By fitting a rotation matrix, we avoid unphysical matrices.…”

Section: Solving For Telescope Mueller Matrix Elementsmentioning

confidence: 99%

Daytime sky polarization calibration limitations

Harrington

Kuhn

Ariste

2017

J. Astron. Telesc. Instrum. Syst

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The daytime sky has been recently demonstrated as a useful calibration tool for deriving polarization cross-talk properties of large astronomical telescopes. The Daniel K Inouye Solar Telescope (DKIST) and other large telescopes under construction can benefit from precise polarimetric calibration of large mirrors. Several atmospheric phenomena and instrumental errors potentially limit the techniques accuracy. At the 3.67m AEOS telescope on Haleakala, we have performed a large observing campaign with the HiVIS spectropolarimeter to identify limitations and develop algorithms for extracting consistent calibrations. Effective sampling of the telescope optical configurations and filtering of data for several derived parameters provide robustness to the derived Mueller matrix calibrations. Second-order scattering models of the sky show that this method is relatively insensitive to multiple-scattering in the sky provided calibration observations are done in regions of high polarization degree. The technique is also insensitive to assumptions about telescope induced polarization provided the mirror coatings are highly reflective. Zemax-derived polarization models show agreement between the functional dependence of polarization predictions and the corresponding on-sky calibrations.

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“…The 4 × 4 polarization transformation matrix, i.e., Mueller matrix, provide a comprehensive representation on the specimen’s polarimetric properties, while non-polarization or other polarization methods only reveal a subset of them. Since polarization properties are closely related to the microstructural features of scattering media 6 , 7 , particularly sensitive to super resolution features at subwavelength scale 8 , 9 , Mueller matrix microscopy is capable of mapping tissue architecture down to subcellular level 9 , 10 . Being treated as an inverse problem, the pixel-level correlation between microstructure and polarization features are usually established by taking supervised learning approaches 11 – 14 , as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning of pixel clustering in Mueller matrix images for mapping microstructural features in pathological tissues

Wan,

Dong,

Yao

et al. 2023

Commun Eng

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In histopathology, doctors identify diseases by characterizing abnormal cells and their spatial organization within tissues. Polarization microscopy and supervised learning have been proved as an effective tool for extracting polarization parameters to highlight pathological features. Here, we present an alternative approach based on unsupervised learning to group polarization-pixels into clusters, which correspond to distinct pathological structures. For pathological samples from different patients, it is confirmed that such unsupervised learning technique can decompose the histological structures into a stable basis of characteristic microstructural clusters, some of which correspond to distinctive pathological features for clinical diagnosis. Using hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) samples, we demonstrate how the proposed framework can be utilized for segmentation of histological image, visualization of microstructure composition associated with lesion, and identification of polarization-based microstructure markers that correlates with specific pathology variation. This technique is capable of unraveling invisible microstructures in non-polarization images, and turn them into visible polarization features to pathologists and researchers.

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Subwavelength metrological chracterization by Mueller matrix polarimeter and finite difference time domain method

Cited by 9 publications

References 17 publications

Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films

Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films

Daytime sky polarization calibration limitations

Unsupervised learning of pixel clustering in Mueller matrix images for mapping microstructural features in pathological tissues

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