Classification of white and gray matter using two-parameter model based on polarization-sensitive optical coherence tomography

In brain imaging, it is necessary to accurately distinguish between white matter, made up of axons and their myelin sheaths, and gray matter, made up of neurons and dendrites. Traditional optical coherence tomography (OCT) utilizes the scattering properties of light from the white and gray matter, however, the accuracy is restricted by the angle between the nerve fibers and the observation plane. In this work, we used polarization-sensitive optical coherence tomography (PSOCT) system to obtain the sample's birefringence information according to phase retardation image and proposed a two-parameter classification model containing reflectivity and birefringence, test the probability of white matter and gray matter using Bayesian detection methods, and adjust the threshold to obtain the best performance. The three-dimensional (3D) volume data were divided into 16 groups on average along the A-line direction. In each group, the white matter was set as 1 and the gray matter was set as 0, thus serving as the mask of the group. By applying multi-masks to 3D data, the position of white matter can be seen accurately. As a result, twice as much white matter was determined by proposed method than conventional method. The scattering and polarization information can be displayed simultaneously by encoding the HSV(Hue, Saturation, Value) model with reflectivity, phase retardation and optical axis orientation respectively. Our proposed two-parameter classification model can be used to distinguish white and gray matter in brain and spinal cord.