Comparison of nonparametric methods for static visual field interpolation

Travis Smith, Ning Smith, Richard Weleber

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalMedical and Biological Engineering and Computing
DOIs
StateAccepted/In press - Apr 22 2016

Fingerprint

Interpolation
Mean square error
Testing
Splines
Display devices
Chemical analysis

Keywords

  • Interpolation
  • Perimetry
  • Retinitis pigmentosa
  • Visual fields

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

Cite this

Comparison of nonparametric methods for static visual field interpolation. / Smith, Travis; Smith, Ning; Weleber, Richard.

In: Medical and Biological Engineering and Computing, 22.04.2016, p. 1-10.

Research output: Contribution to journalArticle

@article{e7b857c1805e4b5c85ce0bcb3dd79a44,
title = "Comparison of nonparametric methods for static visual field interpolation",
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.",
keywords = "Interpolation, Perimetry, Retinitis pigmentosa, Visual fields",
author = "Travis Smith and Ning Smith and Richard Weleber",
year = "2016",
month = "4",
day = "22",
doi = "10.1007/s11517-016-1485-x",
language = "English (US)",
pages = "1--10",
journal = "Medical and Biological Engineering and Computing",
issn = "0140-0118",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Comparison of nonparametric methods for static visual field interpolation

AU - Smith, Travis

AU - Smith, Ning

AU - Weleber, Richard

PY - 2016/4/22

Y1 - 2016/4/22

N2 - 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.

AB - 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.

KW - Interpolation

KW - Perimetry

KW - Retinitis pigmentosa

KW - Visual fields

UR - http://www.scopus.com/inward/record.url?scp=84964222497&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964222497&partnerID=8YFLogxK

U2 - 10.1007/s11517-016-1485-x

DO - 10.1007/s11517-016-1485-x

M3 - Article

AN - SCOPUS:84964222497

SP - 1

EP - 10

JO - Medical and Biological Engineering and Computing

JF - Medical and Biological Engineering and Computing

SN - 0140-0118

ER -