Stochastic decomposition method for modeling the scattered signal reflected of mucosal tissues

Abstract

The aim of this work is to draw the attention of the biophotonics community to a stochastic decomposition method (SDM) to potentially model 2-D scans of light scattering data from epithelium mucosa tissue. The emphasis in this work is on the proposed model and its theoretical pinning and foundation. Unlike previous works that analyze scattering signal at one spot as a function of wavelength or angle, our method statistically analyzes 2-D scans of light scattering data over an area. This allows for the extraction of texture parameters that correlate with changes in tissue morphology, and physical characteristics such as changes in absorption and scattering characteristics secondary to disease, information that could not be revealed otherwise. The method is tested on simulations, phantom data, and on a limited preliminary in-vitro animal experiment to track mucosal tissue inflammation over time, using the area Az under receiver operating characteristics (ROC) curve as a performance measure. Combination of all the features results in an Az value up to 1 for the simulated data, and Az > 0.927 for the phantom data. For the tissue data, the best performances for differentiation between pairs of various levels of inflammation are 0.859, 0.983, and 0.999.

On‐the‐fly detection of changes on and below the surface in epithelium mucosal tissue architecture from scattered light

“…The applicability of the model for differentiating different tissue characteristics using simulations, phantom data and on a limited preliminary in vitro animal experiment for tracking mucosal tissue inflammation over time has been verified and tested in our previous reported work with efficacy given by the area A z under the Receiver Operating Characteristics (ROC) curve by fusing all the estimated parameter set together. Very high A z values were reported (A z value of 1 for simulated data (perfect detector), A z > 0.927 for the phantom data, and A z values of 0.859, 0.983, and 0.999 for differentiation between pairs of various levels of inflammation for the tissue data) [26][27][28]. For our onthe-fly segmentation scheme, tests are performed on both tissue mimicking phantoms and real tissue data (in vitro).…”

“…In this paper, our goal is to capture on-the-fly where changes in the tissue mucosal structures occur with no prior knowledge of their structure(s) by introducing a segmentation algorithm based on the textural model parameters of the reflected data obtained from the Stochastic Decomposition Method (SDM) [26][27][28]. This manuscript describes a variation of light scattering spectroscopy similar to the work originally introduced in [29], but employs a somewhat different data processing scheme.…”

“…The SDM consists of taking the wavelet transform of the scattered signal and thresholding its energy. More detailed information on the theory of this decomposition is given in our previous work [26]. For a totally diffuse component d(n) (which is the case considered in this paper) is a zero mean autoregressive process of order p (AR(p)).…”

“…(3). What determines p is the memory of the process, namely, how many previous values of the process enter in the determination of the conditional mean value of the process at time n. The value of 5 for p is determined using our previous work in [26,32,34]. The AR process is parameterized by the set…”

“…The SDM model [26][27][28] uses reflected light at one specific wavelength l. SDM method is consistent Figure 1 Layered structure and principal histological components of normal, dysplastic, and adenomatous colon tissue [36]. Figure 2 Schematic representation of colon architecture [16].…”