Autofluorescence characteristics of normal foveas and reconstruction of foveal autofluorescence from limited data subsets.

PURPOSE: To develop mathematical and geometric models of the nonuniform autofluorescence (AF) patterns of foveas of normal subjects and to reconstruct these models from limited subsets of data. METHODS: Confocal scanning laser ophthalmoscope (cSLO) AF fundus images of normal maculae were obtained from both eyes of 10 middle-aged subjects. They were filtered and contrast enhanced, to obtain elliptical isobars of equal gray levels (GLs) and determine the isobars' resolutions, eccentricities, and angles of orientation. The original image data were fit with a mathematical model of elliptic quadratic polynomials in two equal zones: the center and the remaining annulus. RESULTS: The AF images segmented into nested concentric GL isobars with GLs that increased radially from the least-fluorescent center. The mean isobar resolution was 31 +/- 7 mum. The geometric eccentricity of the ellipses increased from 0.42 +/- 0.12 centrally to 0.52 +/- 0.14 peripherally (P = 0.0005), with mean axes of orientation peripherally 97.12 +/- 15.46 degrees . The model fits to the complete image data had mean absolute normalized errors ranging from 3.6% +/- 3.7% to 7.3% +/- 7.1%. The model fits to small subsets (1% to 2% of total image data) had mean absolute errors ranging from 3.7% +/- 3.8% to 7.3% +/- 7.2%. CONCLUSIONS: Normal AF fundus images show finely resolved, concentric, elliptical foveal patterns consistent with the anatomic distribution of fluorescent lipofuscin, light-attenuating macular pigment (MP), cone photopigment, and retinal pigment epithelial (RPE) pigment in the fovea. A two-zone, elliptic, quadratic polynomial model can accurately model foveal data. This model may be useful for image analysis and for automated segmentation of pathology.

Photographic patterns in macular images: representation by a mathematical model.

Normal macular photographic patterns are geometrically described and mathematically modeled. Forty normal color fundus photographs were digitized. The green channel gray-level data were filtered and contrast enhanced, then analyzed for concentricity, convexity, and radial resolution. The foveal data for five images were fit with elliptic quadratic polynomials in two zones: a central ellipse and a surrounding annulus. The ability of the model to reconstruct the entire foveal data from selected pixel values was tested. The gray-level patterns were nested sets of concentric ellipses. Gray levels increased radially, with retinal vessels changing the patterns to star shaped in the peripheral fovea. The elliptic polynomial model could fit a high-resolution green channel foveal image with mean absolute errors of 6.1% of the gray-level range. Foveal images were reconstructed from small numbers of selected pixel values with mean errors of 7.2%. Digital analysis of normal fundus photographs shows finely resolved concentric elliptical foveal and star-shaped parafoveal patterns, which are consistent with anatomical structures. A two-zone elliptic quadratic polynomial model can approximate foveal data, and can also reconstruct it from small subsets, allowing improved macular image analysis.