Improved Diagnostics Using Polarization Imaging and Artificial Neural Networks

In recent years there has been an increasing interest in studying the propagation of polarized light in randomly scattering media. This paper presents a novel approach for cell and tissue imaging by using full Stokes imaging and for its improved diagnostics by using artificial neural networks (ANNs). Phantom experiments have been conducted using a prototyped Stokes polarization imaging device. Several types of phantoms, consisting of polystyrene latex spheres in various diameters, were prepared to simulate different conditions of epidermal layer of skin. Several sets of four images that contain not only the intensity, but also the polarization information were taken for analysis. Wavelet transforms are first applied to the Stokes components for initial feature analysis and extraction. Artificial neural networks (ANNs) are then used to extract diagnostic features for improved classification and prediction. The experimental results show that the classification performance using Stokes images is significantly improved over that using the intensity image only.