Noise stochastic corrected maximum a posteriori estimator for birefringence imaging using polarization-sensitive optical coherence tomography

Abstract

This paper presents a noise-stochastic corrected maximum a posteriori estimator for birefringence imaging using Jones matrix optical coherence tomography. The estimator described in this paper is based on the relationship between probability distribution functions of the measured birefringence and the effective signal to noise ratio (ESNR) as well as the true birefringence and the true ESNR. The Monte Carlo method is used to numerically describe this relationship and adaptive 2D kernel density estimation provides the likelihood for a posteriori estimation of the true birefringence. Improved estimation is shown for the new estimator with stochastic model of ESNR in comparison to the old estimator, both based on the Jones matrix noise model. A comparison with the mean estimator is also done. Numerical simulation validates the superiority of the new estimator. The superior performance of the new estimator was also shown by in vivo measurement of optic nerve head.

Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography

“…The limitation of cumulative phase retardation imaging can be overcome by the birefringence measurement based on localized Jones matrix measurement [23][24][25]. Although the local Jones matrix measurement suffers from a low signalto-noise ratio and inaccurate phase-retardation and birefringence, these problems were recently resolved by birefringence and phase retardation estimation techniques that include the coherent averaging of the Jones matrix and/or Jones vector [26][27][28], eigen-value-based averaging [29], Jones matrix estimation based on the Cloude-Pottier decomposition [30], Muller analysis [31,32], a specially designed phase-retardation estimation function [33], and maximum a posteriori birefringence estimators [25,34,35]. These advances enabled the birefringence imaging of in vivo skin.…”

Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

“…One possible solution is combination of maximum a-posteriori (MAP) birefringence estimator [8,13] and the superpixel kernel. Since our particular method has used the MAP estimator for the initial estimation of birefringence, this secondary estimation process can be considered as "re-estimation" of birefringence with an adaptive kernel.…”

“…Polarization sensitive OCT (PS-OCT) provides not only structural information but also the polarization properties of the sample [4,5]. Among the polarization properties, local birefringence (BR) is considered to provide collagen contrast [6][7][8][9][10][11][12][13]. The degree of polarization uniformity (DOPU) is another polarization property [14][15][16][17].…”

“…Similarly, DOPU is computed as a circular variance of Stokes vectors in a local region [14]. BR is also estimated using signals in a small local region [8,13]. Conventionally, these local statistics are computed using a fixed-size rectangular kernel.…”

Multi-contrast imaging of human posterior eye by Jones matrix optical coherence tomography

“…The third tomography is birefringence (local phase retardation) tomography. It is a local phase retardation computed from depth-localized Jones matrices 7 , but its quantitative accuracy has been enhanced by MAP birefringence estimators 8,9 . The fourth tomography is polarization uniformity tomography.…”