Mapping of retardance, diattenuation and polarizance vector on Poincare sphere for diagnosis and classification of cervical precancer

Abstract

The mapping of diattenuation, polarizance and retardance vector (normalized Stokes vector) on Poincare sphere, evaluated from Mueller matrix of optically anisotropic stromal region of cervical tissues, is presented for cervical precancer detection and its staging. This reveals that the changes in the polarization states shown by these normalized Stokes vectors corresponds to the degradation of linearly arranged collagen fibers, breakage of the collagen cross links in the stromal region and change in the density of scattering sites when cervical cancer evolves. The distinct nature of real and imaginary parts of the refractive index for linear, linear-45 and circular polarization from the optically anisotropic stromal region underscore the various polarization structures of the connective tissue region which get modified during the pathological changes. It has been found that versatility of these vectors for normal and precancerous cervical tissue of various grades may be utilized as a key distinction for qualitative staging of cervical precancer tissue. Quantitative classification of precancerous stages of cervical precancer has been determined with 95%-100% sensitivity and 93%-100% specificity through the evaluation of linear and circular diattenuation, linear polarizance and linear birefringence from the components of the respective vectors. K E Y W O R D S cervical cancer, diattenuation vector, Mueller matrix, Poincare sphere, polarizance vector, retardance vector, stromal region

Colon cancer detection by using Poincaré sphere and 2D polarimetric mapping of ex vivo colon samples

“…Finally, our conclusion can be extended in a brief comparison with results obtained from various groups in the field of tissue polarimetry, whereas Sridhad et al [49] achieved better sensitivity, higher contrast and signal intensity for 633 nm with elliptical polarization than linear. Another important study in the NIR spectral region in terms of Mueller-matrix imaging spectroscopy by Wang et al [50] unambiguously presents lesser depolarization coefficient for colonic cancer, while Zaffar et al [51] utilized Poincaré sphere representation in order to differ between pre-cancerous and normal tissues.…”

Polarization multi-parametric imaging method for the inspection of cervix cell

“…In addition, polarization-based imaging methods have also shown great potential in detecting structural and optical information about the tissues and cells [12][13][14][15][16]. As a result of their capability of obtaining abundant structural information of the tissue, polarization techniques are promising techniques for early screening and identification of cancers [17][18][19][20][21][22][23][24][25][26]. Here, we propose a fast polarization-based multiparametric method, Polarization Indirect Microscopic Imaging (PIMI), for the inspection of cervical cells with nano-structural resolving power and sensitivity.…”

Polarization clustering of biological structures with Mueller matrix parameters

“…Digital image processing can be used to enhance the image textural quality for better quantitative analysis, providing objective and reliable results [1]. As a potential digital staining technique, polarization imaging has been extensively utilized to investigate the structural abnormality in pathological tissue diagnosis [2–5], including breast cancer [6], liver fibrosis [7], cervical precancer [8], kidney [9], muscle [10], and others. The Mueller matrix (MM) offers a complete mathematical description of the polarization properties of an object, and thus MM imaging polarimetry (MMIP) has emerged as an essential method for characterizing biological structures, which comprises a number of MM parameters (MMPs) [11, 12].…”

“…To deal with this problem, many multiparameter fusion methods (MPFMs) have been proposed. For example, different MMPs were combined in a single polarization staining image based on RGB and HSI color spaces [15], a linear discriminant analysis (LDA) classifier was used to find the most simplified linear combination from polarimetry basis parameters to characterize the microstructures in typical breast tissues [16], polarization space was presented for cervical precancer detection and its staging [8], images of breast cancer samples were automatically segmented by the k-means cluster analysis of the detected Stokes vectors [17], and biological tissues were classified by depolarization space [18,19]. The MMPs are the cornerstone of MPFM, and it is advantageous to generate more MMPs that can distinguish biological structures.…”