Improving dOCT image quality with short sequences and automated binning

Abstract

Dynamic optical coherence tomography (dOCT) uses signal fluctuations to contrast different cells and tissues. In this paper, we demonstrate that shortening the time base over which the signal fluctuations are evaluated reduces noise induced by motion while still maintaining a decent image quality. Automatic clustering using the neural-gas algorithm is introduced to optimize the border between the color channels. The performance of the automatic border optimization is demonstrated with 15 different tissue samples by quantitative assessment of motion-induced noise and image quality using the mean squared error (MSE) between images and the image quality parameters peak signal to noise ratio (PSNR) and structural similarity (SSIM).