Template free eye motion correction for scanning systems

Automatic montaging of adaptive optics SLO retinal images based on graph theory

We present a fully automatic montage pipeline for adaptive optics SLO retinal images. It contains a flexible module to estimate the translation between pairwise images. The user can change modules to accommodate the alignment of the dataset using the most appropriate alignment technique, provided that it estimates the translation between image pairs and provides a quantitative confidence metric for the match between 0 and 1. We use these pairwise comparisons and associated metrics to construct a graph where nodes represent frames and edges represent the overlap relations. We use a small diameter spanning tree to determine the best pairwise alignment for each image based on the entire set of image relations. The final stage of the pipeline is a blending module that uses dynamic programming to improve the smoothness of the transition between frames. Data sets ranging from 26 to 119 images were obtained from individuals aged 24 to 81 years with a mix of visually normal control eyes and eyes with glaucoma or diabetes. The resulting automatically generated montages were qualitatively and quantitatively compared to results from semi-automated alignment. Data sets were specifically chosen to include both high quality and medium quality data. The results obtained from the automatic method are comparable or better than results obtained by an experienced operator performing semi-automated montaging. For the plug-in pairwise alignment module, we tested a technique that utilizes SIFT + RANSAC, Normalized cross-correlation (NCC) and a combination of the two. This pipeline produces consistent results not only on outer retinal layers, but also on inner retinal layers such as a nerve fiber layer or images of the vascular complexes, even when images are not of excellent quality.

Evolution of adaptive optics retinal imaging [Invited]

This review describes the progress that has been achieved since adaptive optics (AO) was incorporated into the ophthalmoscope a quarter of a century ago, transforming our ability to image the retina at a cellular spatial scale inside the living eye. The review starts with a comprehensive tabulation of AO papers in the field and then describes the technological advances that have occurred, notably through combining AO with other imaging modalities including confocal, fluorescence, phase contrast, and optical coherence tomography. These advances have made possible many scientific discoveries from the first maps of the topography of the trichromatic cone mosaic to exquisitely sensitive measures of optical and structural changes in photoreceptors in response to light. The future evolution of this technology is poised to offer an increasing array of tools to measure and monitor in vivo retinal structure and function with improved resolution and control.

Strip-based digital image registration for distortion minimization and robust eye motion measurement from scanned ophthalmic imaging systems

Retinal image-based eye motion measurement from scanned ophthalmic imaging systems, such as scanning laser ophthalmoscopy, has allowed for precise real-time eye tracking at sub-micron resolution. However, the constraints of real-time tracking result in a high error tolerance that is detrimental for some eye motion measurement and imaging applications. We show here that eye motion can be extracted from image sequences when these constraints are lifted, and all data is available at the time of registration. Our approach identifies and discards distorted frames, detects coarse motion to generate a synthetic reference frame and then uses it for fine scale motion tracking with improved sensitivity over a larger area. We demonstrate its application here to tracking scanning laser ophthalmoscopy (TSLO) and adaptive optics scanning light ophthalmoscopy (AOSLO), and show that it can successfully capture most of the eye motion across each image sequence, leaving only between 0.1-3.4% of non-blink frames untracked, while simultaneously minimizing image distortions induced from eye motion. These improvements will facilitate precise measurement of fixational eye movements (FEMs) in TSLO and longitudinal tracking of individual cells in AOSLO.