Influence of sampling window size and orientation on parafoveal cone packing density

Lombardo, Marco; Serrao, Sebastiano; Ducoli, Pietro; Lombardo, Giuseppe

doi:10.1364/boe.4.001318

Cited by 25 publications

(51 citation statements)

References 41 publications

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“…The results from the present work illustrated the reliability of using Voronoi analysis to compare data of 6 n preferred packing arrangements taken from sampling areas of different size in the same subject or between subjects [15], [32]. Overall, the graphical representation of the cone mosaic geometry is less sensitive to window size than cone density, as previously shown [15].…”

Section: Discussionsupporting

confidence: 73%

“…The corrected magnification factor (RMF corr ) was calculated for each eye in order to correct for the differences in optical magnification and thus retinal image size between eyes [13], [15], [18].…”

Section: Methodsmentioning

confidence: 99%

“…The cone image labelling process was performed using an algorithm implemented with the Image Processing toolbox in Matlab (The Mathworks Inc, Natick MA, USA), as previously described in detail [1], [13], [15]. Cones were identified independently in each sampling window.…”

Section: Methodsmentioning

confidence: 99%

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Technical Factors Influencing Cone Packing Density Estimates in Adaptive Optics Flood Illuminated Retinal Images

2014

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PurposeTo investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images.MethodsAdaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL). The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr), the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions.ResultsThe cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams.ConclusionsThe sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi diagrams of the cone mosaic.

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Section: Discussionsupporting

confidence: 73%

Section: Methodsmentioning

confidence: 99%

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Technical Factors Influencing Cone Packing Density Estimates in Adaptive Optics Flood Illuminated Retinal Images

2014

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“…Therefore, the clinical and research utility of adaptive optics remains limited by a lack of automated cone sampling and density representation methods. Additionally, although some studies have explored the effect of differentsized sampling windows on measurements of cone density, 43 it is still unclear how to best represent or sample cone density most accurately and consistently.…”

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confidence: 99%

Assessment of Different Sampling Methods for Measuring and Representing Macular Cone Density Using Flood-Illuminated Adaptive Optics

Feng

Gale

Fay

et al. 2015

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. To describe a standardized flood-illuminated adaptive optics (AO) imaging protocol suitable for the clinical setting and to assess sampling methods for measuring cone density.METHODS. Cone density was calculated following three measurement protocols: 50 3 50-lm sampling window values every 0.58 along the horizontal and vertical meridians (fixed-interval method), the mean density of expanding 0.58-wide arcuate areas in the nasal, temporal, superior, and inferior quadrants (arcuate mean method), and the peak cone density of a 50 3 50-lm sampling window within expanding arcuate areas near the meridian (peak density method). Repeated imaging was performed in nine subjects to determine intersession repeatability of cone density. RESULTS.Cone density montages could be created for 67 of the 74 subjects. Image quality was determined to be adequate for automated cone counting for 35 (52%) of the 67 subjects. We found that cone density varied with different sampling methods and regions tested. In the nasal and temporal quadrants, peak density most closely resembled histological data, whereas the arcuate mean and fixed-interval methods tended to underestimate the density compared with histological data. However, in the inferior and superior quadrants, arcuate mean and fixed-interval methods most closely matched histological data, whereas the peak density method overestimated cone density compared with histological data. Intersession repeatability testing showed that repeatability was greatest when sampling by arcuate mean and lowest when sampling by fixed interval. CONCLUSIONS.We show that different methods of sampling can significantly affect cone density measurements. Therefore, care must be taken when interpreting cone density results, even in a normal population.

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“…The cropped window area of an image (256×256 pixels) corresponded to ~0.41°×0.33° on the scan angle of our AOSLO. This window area is approximately equivalent to 120×100 µm at 24 mm axial length, which is enough to estimate cone densities 24. The square image area is not equivalent to the square area on the retina because of the correction for the distortion of the raw digital image.…”

Section: Methodsmentioning

confidence: 99%

Automated measurements of human cone photoreceptor density in healthy and degenerative retina by region-based segmentation

Miyagawa

Fukuyama

Hirota

et al. 2017

OPTH

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PurposeThe purpose of this study was to develop an algorithm based on region-based segmentation for automated calculations of human cone photoreceptor density of en face images obtained by an adaptive optics scanning laser ophthalmoscope (AOSLO).Subjects and methodsCone mosaics of 15 eyes of 15 healthy subjects were photographed by a custom-built AOSLO. The cone density was calculated at 0.5, 1.0, and 1.5 mm temporal from the fovea using a region-based segmentation method (RSM) developed in our laboratory. The cone density was also determined by a manual identification method (MIM) and a conventional spatial filtering method (SFM). The cone densities of three eyes of three patients with retinal degeneration were calculated by the three methods and compared to the results from normal eyes.ResultsThe cone densities in healthy retinas determined by the RSM at 0.5, 1.0, and 1.5 mm temporal from the fovea were 28,436, 21,233, and 13,620 cells/mm2, respectively. These densities were in good agreement with a histological study and with in vivo AOSLO studies. The cone densities determined by RSM were different from those determined by MIM with a difference of 5% in healthy eyes. In eyes with retinal degeneration, with the appropriate threshold-level settings or spatial frequency bandwidth, the cone density measured by MIM was significantly closer to that measured by RSM than by SFM.ConclusionThese results suggest that our method is more stable than conventional methods in cases of non-periodical photoreceptor structures such as the affected retinal area. Our method can be used in the longitudinal follow-up of retinal degenerative diseases and to determine the effect of therapy.

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Influence of sampling window size and orientation on parafoveal cone packing density

Cited by 25 publications

References 41 publications

Technical Factors Influencing Cone Packing Density Estimates in Adaptive Optics Flood Illuminated Retinal Images

Technical Factors Influencing Cone Packing Density Estimates in Adaptive Optics Flood Illuminated Retinal Images

Assessment of Different Sampling Methods for Measuring and Representing Macular Cone Density Using Flood-Illuminated Adaptive Optics

Automated measurements of human cone photoreceptor density in healthy and degenerative retina by region-based segmentation

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