Registration of adaptive optics corrected retinal nerve fiber layer (RNFL) images

Abstract

Glaucoma is the leading cause of preventable blindness in the western world. Investigation of high-resolution retinal nerve fiber layer (RNFL) images in patients may lead to new indicators of its onset. Adaptive optics (AO) can provide diffraction-limited images of the retina, providing new opportunities for earlier detection of neuroretinal pathologies. However, precise processing is required to correct for three effects in sequences of AO-assisted, flood-illumination images: uneven illumination, residual image motion and image rotation. This processing can be challenging for images of the RNFL due to their low contrast and lack of clearly noticeable features. Here we develop specific processing techniques and show that their application leads to improved image quality on the nerve fiber bundles. This in turn improves the reliability of measures of fiber texture such as the correlation of Gray-Level Co-occurrence Matrix (GLCM).

Adaptive optics optical coherence tomography in glaucoma

“…The increased ability to visualize and quantify these nerve fiber bundles, which have also been demonstrated in flood-illuminated ophthalmoscopes and SLO (Huang et al, 2012; Takayama et al, 2012, Ramaswamy et al 2014), have allowed for examination of differences in the bundles themselves and the observation that RNFL bundles possess a discrete reflectivity pattern. One study imaged the RNFL with AO-OCT in four healthy subjects and reported that the RNFL bundles reflect two times more light than the surrounding tissue (Kocaoglu et al, 2011a).…”

“…Understandably, fundus cameras were the first imaging devices to implement AO technology in ophthalmology, as they employ simple optical principles involving little more than a camera capturing the light originating on the retina and emerging from the eye's optics to form an image on a light-sensitive film or charge coupled device. AO fundus cameras have since been used to image multiple structures in the eye, including photoreceptors (Lombardo et al, 2012), the RNFL (Ramaswamy et al, 2014) and retinal microvasculature (Popovic et al, 2011). …”

An Automated Reference Frame Selection (ARFS) Algorithm for Cone Imaging with Adaptive Optics Scanning Light Ophthalmoscopy

“…Adaptations of other methods may bridge this gap. 52 Further, ARFS performance has not been rigorously tested in image sequences where there is significant disruption to the cone mosaic such that ARFS may mistake aberrantly shaped or organized cones as being compressed or stretched. Subjectively, sampling a large strip of the retina typically makes the intraframe motion module robust to such disruptions, and tuning this parameter may resolve this issue.…”

“…Algorithms that employ template-independent image quality metrics (e.g., sharpness, contrast, brightness) or statistical methods for automated reference frame selection from AO-flood, 51–53 AO-LSO, 40 and AO-telescopes 54 have been proposed; however, these metrics are not robust for detecting motion artifacts within AOSLO images. Unsupervised registration algorithms of raw AO-flood image sequences have been developed, 55,56 however their efficacy in handling large motion artifacts has not been demonstrated.…”

Adaptive Optics Ophthalmoscopy

“…The NFL is a relatively strong source of back-scatter and its microstructure has been resolved with confocal AOSLO imaging (Huang et al 2014, Takayama et al 2012) and AO fundus cameras (Ramaswamy et al 2014). Fig.…”