Fast adaptive optics scanning light ophthalmoscope retinal montaging

Abstract

The field of view of high-resolution ophthalmoscopes that require the use of adaptive optics (AO) wavefront correction is limited by the isoplanatic patch of the eye, which varies across individual eyes and with the portion of the pupil used for illumination and/or imaging. Therefore all current AO ophthalmoscopes have small fields of view comparable to, or smaller than, the isoplanatic patch, and the resulting images have to be stitched off-line to create larger montages. These montages are currently assembled either manually, by expert human graders, or automatically, often requiring several hours per montage. This arguably limits the applicability of AO ophthalmoscopy to studies with small cohorts and moreover, prevents the ability to review a real-time captured montage of all locations during image acquisition to further direct targeted imaging. In this work, we propose stitching the images with our novel algorithm, which uses oriented fast rotated brief (ORB) descriptors, local sensitivity hashing, and by searching for a ‘good enough’ transformation, rather than the best possible, to achieve processing times of 1–2 minutes per montage of 250 images. Moreover, the proposed method produces montages which are as accurate as previous methods, when considering the image similarity metrics: normalised mutual information (NMI), and normalised cross correlation (NCC).

Intraobserver Repeatability and Interobserver Reproducibility of Foveal Cone Density Measurements in CNGA3- and CNGB3-Associated Achromatopsia

“…The final images were combined into a single montage (Adobe Photoshop; Adobe Systems, Inc., San Jose, CA, USA) either manually or semiautomattically. 34 Where possible, images taken using 1°field of view were analyzed due to their higher resolution.…”

Photoreceptor Structure inGNAT2-Associated Achromatopsia

“…The final images were combined into a single montage (Adobe Photoshop, Adobe Systems Inc., San Jose, CA, USA) in layers for the different detection schemes using a custom-built automated software. 39 The scale was determined first in degrees per pixel in an image of a Ronchi ruling of known spacing after each imaging session. Then, the value was linearly scaled using the subject's axial length.…”

Automatic longitudinal montaging of adaptive optics retinal images using constellation matching

“…Such false positives will impact the accuracy of the transformation estimation and must be removed for accurate cell-to-cell alignment. To address this problem, we use a technique known as random sample consensus (RANSAC) [17] to remove outlier matches, which has been effective for within timepoint AO montaging [6][7][8]. The goal of RANSAC is to estimate multiple prospective transformations, T R , from random subsets of matched feature pairs in F a↔b .…”

“…To analyze a full AO acquisition, individual images must be aligned and montaged to cover larger regions of the retina for analysis. Several algorithms [6][7][8] have been introduced that maximize a measure of intensity similarity to automatically montage AO images from the same imaging session. However, monitoring retinal change requires accurate longitudinal montaging that provides cell-to-cell alignment between images taken across timepoints that differ on the scale of weeks, months and years.…”