Abstract
Visual field testing with standard automated perimetry produces a sparse representation of a sensitivity map, sometimes called the hill of vision (HOV), for the retina. Interpolation or resampling of these data is important for visual display, clinical interpretation, and quantitative analysis. Our objective was to compare several popular interpolation methods in terms of their utility to visual field testing. We evaluated nine nonparametric scattered data interpolation algorithms and compared their performances in normal subjects and patients with retinal degeneration. Interpolator performance was assessed by leave-one-out cross-validation accuracy and high-density interpolated HOV surface smoothness. Radial basis function (RBF) interpolation with a linear kernel yielded the best accuracy, with an overall mean absolute error (MAE) of 2.01 dB and root-mean-square error (RMSE) of 3.20 dB that were significantly better than all other methods (p ≤ 0.003). Thin-plate spline RBF interpolation yielded the best smoothness results (p < 0.001) and scored well for accuracy with overall MAE and RMSE values of 2.08 and 3.28 dB, respectively. Natural neighbor interpolation, which may be a more readily accessible method to some practitioners, also performed well. While no interpolator will be universally optimal, these interpolators are good choices among nonparametric methods.
Highlights
Interpolation of static visual field sensitivity data offers several benefits
Surface renderings of the hill of vision (HOV) are helpful to clinicians in disease monitoring and treatment, and in providing a visual model to aid in discussions of examination results with patients [13]
Each perimetry examination produced a 3-D point cloud of triplets {(xi, yi, zi)}Ni=1 with N = 164 test points where each point was represented by a retinal location (xi, yi) in angular coordinates and differential luminance sensitivity, which was the light sensitivity in dB measured at that location
Summary
Interpolation of static visual field sensitivity data offers several benefits. By resampling examination data onto a single common grid, it can unify disparate perimetry protocols. This facilitates the joint analysis of examinations conducted under different studies or with different test grid patterns, which otherwise would be difficult to compare. Interpolation is useful for transforming data from irregular or sparse grids into a uniformly sampled format. Resampling onto a high-density regular grid produces a surface representation of the hill of vision (HOV) which offers more flexibility in visualizing examination results than conventional display methods that show only discrete numerical or coarsely quantized information [15, 26]. Topographic interpretation of interpolated HOV surfaces supports contour-based and volumetric approaches to visual field analysis [27]
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