Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images

Abstract

The ability to non-invasively image the cone photoreceptor mosaic holds significant potential as a diagnostic for retinal disease. Central to the realization of this potential is the development of sensitive metrics for characterizing the organization of the mosaic. Here we evaluated previously-described (Pum et al., 1990) and newly-developed (Fourier- and Radon-based) methods of measuring cone orientation in both simulated and real images of the parafoveal cone mosaic. The proposed algorithms correlated well across both simulated and real mosaics, suggesting that each algorithm would provide an accurate description of individual photoreceptor orientation. Despite the high agreement between algorithms, each performed differently in response to image intensity variation and cone coordinate jitter. The integration property of the Fourier transform allowed the Fourier-based method to be resistant to cone coordinate jitter and perform the most robustly of all three algorithms. Conversely, when there is good image quality but unreliable cone identification, the Radon algorithm performed best. Finally, in cases where both the image and cone coordinate reliability was excellent, the method of Pum et al. (1990) performed best. These descriptors are complementary to conventional descriptive metrics of the cone mosaic, such as cell density and spacing, and have the potential to aid in the detection of photoreceptor pathology.

Reliability and Agreement Between Metrics of Cone Spacing in Adaptive Optics Images of the Human Retinal Photoreceptor Mosaic

“…Several other metrics have been generated from the point coordinates of cells or directly from the AO image of the cone mosaic, such as those based on analysis of the Fourier spectrum of the image. 13,[32][33][34][35][36][37][38][39] Currently, the main limit of any metric describing the spatial position of the cones is related to the correct cell identification. As disease progresses, cell loss and disorder in cell spacing increases, which in turn decreases resolution by distorting the AO image of the cone mosaic.…”

Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging

“…To quantify the anisotropy of the mosaic, one can extract the “orientation” of each submosaic, defined by the rotation of this polygon. 60 – 62 This can be done on the scale of an individual submosaic, 60 , 62 or as an average of submosaics. 61 , 62 In addition, the modal spacing of the cones (also referred to as intercell distance [ICD]) can be extracted directly from the Fourier transform of the image, which for normal retinas will have an annular appearance – this annulus often is called “Yellott's ring”.…”

“… 60 – 62 This can be done on the scale of an individual submosaic, 60 , 62 or as an average of submosaics. 61 , 62 In addition, the modal spacing of the cones (also referred to as intercell distance [ICD]) can be extracted directly from the Fourier transform of the image, which for normal retinas will have an annular appearance – this annulus often is called “Yellott's ring”. 61 , 63 – 65 There are limitations to this method in that the power spectrum contains information about the object profile itself in addition to the spacing of the objects in the image; this is less of an issue in a contiguously packed mosaic of cells of uniform size, but becomes disabling when working with images of the peripheral photoreceptor mosaic.…”

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

“…We compared the level of residual distortion in images processed by ARFS and one expert human observer (RFC), assessed the accuracy of motion tracking, and determined how frequently a group of human observers select one of the least distorted frames in a sequence identified by ARFS. ARFS may reduce the data backlog inherent in AO imaging and allow researchers and clinicians to focus on analyzing 61 and interpreting 62 AO images of the retina.…”