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Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions. [ABSTRACT FROM AUTHOR]

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions.

Motivation: Imaging Mueller polarimetry has already proved its potential for biomedicine, remote sensing and metrology. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical data, ie to test the positive semi-definiteness of the 4 × 4 Hermitian coherency matrix calculated from the elements of corresponding Mueller matrix pixel-wise. For this purpose, we compared the execution time for the calculations of i) eigenvalues, ii) Cholesky decomposition, iii) Sylvester's criterion, and iv) coefficients of the characteristic polynomial (two different approaches) of the Hermitian coherency matrix, all calculated for the experimental Mueller matrix images (600 pixels × 700 pixels) of mouse uterine cervix. The calculations were performed using C ++ and Julia programming languages., Results: Our results showed the superiority of the algorithm iv) based on the simplification via Pauli matrices over other algorithms for our dataset. The sequential implementation of latter algorithm on a single core already satisfies the requirements of real-time polarimetric imaging. This can be further amplified by the proposed parallelization (e.g., we achieve a 5-fold speed up on 6 cores)., Availability and Implementation: The source codes of the algorithms and experimental data are available at https://github.com/pogudingleb/mueller_matrices., (© The Author(s) 2024. Published by Oxford University Press.)