Improving retinal image quality using registration with an SIFT algorithm in quasi-confocal line scanning ophthalmoscope

Abstract

When high-magnification images are taken with a quasi-confocal line scanning ophthalmoscope (LSO), the quality of images always suffers from Gaussian noise, and the signal to noise ratio (SNR) is very low for a safer laser illumination. In addition, motions of the retina severely affect the stabilization of the real-time video resulting in significant distortions or warped images. We describe a scale-invariant feature transform (SIFT) algorithm to automatically abstract corner points with subpixel resolution and match these points in sequential images using an affine transformation. Once n images are aligned and averaged, the noise level drops by a factor of n and the image quality is improved. The improvement of image quality is independent of the acquisition method as long as the image is not warped, particularly severely during confocal scanning. Consequently, even better results can be expected by implementing this image processing technique on higher resolution images.