Quantifying Visual Acuity from Image Quality Metrics Article Swipe
Wavefront error (WFE) of the eye has been shown to vary from individual to individual and within an individual as a function of pupil diameter. Of current interest in the area of refractive correction development is the relationship between the aberration experienced by an eye, and resulting visual performance. In a set of experiments, WFE for both normal and keratoconic eyes were selected from published datasets. These WFEs were then used to generate computationally blurred logMAR acuity charts, simulating retinal image quality under a variety of test conditions. Test subjects read the computationally blurred charts to assess the impact of the residual WFE on resulting acuity. These experiments demonstrated that the visual quality metric visual Strehl calculated in the frequency domain (VSOTF) can detect blur before clinically significant change in acuity occurs. These experiments also demonstrated that change in log neural sharpness (logNS) and log visual Strehl ratio in the spatial domain (logVSX) were both highly correlated and predictive of change in VA in both the normal and highly aberrated eyes, independent of pupil diameter. Studies are in progress that utilize these metrics to objectively predict sphero-cylindrical correction, to optimize the design of wavefront guided corrections, and to evaluate optical corrections objectively for both normal and highly aberrated eyes prior to dispensation of the correction.
Related Topics
Concepts
Strehl ratio
Wavefront
Pupil
Computer science
Visual acuity
Artificial intelligence
Foveal
Residual
Image quality
Adaptive optics
Metric (unit)
Computer vision
Zernike polynomials
Visualization
Optics
Image (mathematics)
Algorithm
Physics
Retinal
Medicine
Ophthalmology
Operations management
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1167/16.4.37
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2313587674
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2313587674Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1167/16.4.37Digital Object Identifier
- Title
-
Quantifying Visual Acuity from Image Quality MetricsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-02-12Full publication date if available
- Authors
-
Ayeswarya RavikumarList of authors in order
- Landing page
-
https://doi.org/10.1167/16.4.37Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1167/16.4.37Direct OA link when available
- Concepts
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Strehl ratio, Wavefront, Pupil, Computer science, Visual acuity, Artificial intelligence, Foveal, Residual, Image quality, Adaptive optics, Metric (unit), Computer vision, Zernike polynomials, Visualization, Optics, Image (mathematics), Algorithm, Physics, Retinal, Medicine, Ophthalmology, Operations management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.before | 125 |
| abstract_inverted_index.change | 128, 137, 160 |
| abstract_inverted_index.charts | 94 |
| abstract_inverted_index.design | 191 |
| abstract_inverted_index.detect | 123 |
| abstract_inverted_index.domain | 120, 151 |
| abstract_inverted_index.guided | 194 |
| abstract_inverted_index.highly | 155, 168, 206 |
| abstract_inverted_index.impact | 98 |
| abstract_inverted_index.logMAR | 75 |
| abstract_inverted_index.metric | 113 |
| abstract_inverted_index.neural | 140 |
| abstract_inverted_index.normal | 57, 166, 204 |
| abstract_inverted_index.visual | 47, 111, 114, 145 |
| abstract_inverted_index.within | 16 |
| abstract_inverted_index.(VSOTF) | 121 |
| abstract_inverted_index.(logNS) | 142 |
| abstract_inverted_index.Studies | 175 |
| abstract_inverted_index.acuity. | 105 |
| abstract_inverted_index.between | 38 |
| abstract_inverted_index.blurred | 74, 93 |
| abstract_inverted_index.charts, | 77 |
| abstract_inverted_index.current | 26 |
| abstract_inverted_index.metrics | 182 |
| abstract_inverted_index.occurs. | 131 |
| abstract_inverted_index.optical | 199 |
| abstract_inverted_index.predict | 185 |
| abstract_inverted_index.quality | 81, 112 |
| abstract_inverted_index.retinal | 79 |
| abstract_inverted_index.spatial | 150 |
| abstract_inverted_index.utilize | 180 |
| abstract_inverted_index.variety | 84 |
| abstract_inverted_index.(logVSX) | 152 |
| abstract_inverted_index.evaluate | 198 |
| abstract_inverted_index.function | 21 |
| abstract_inverted_index.generate | 72 |
| abstract_inverted_index.interest | 27 |
| abstract_inverted_index.optimize | 189 |
| abstract_inverted_index.progress | 178 |
| abstract_inverted_index.residual | 101 |
| abstract_inverted_index.selected | 62 |
| abstract_inverted_index.subjects | 89 |
| abstract_inverted_index.Wavefront | 0 |
| abstract_inverted_index.aberrated | 169, 207 |
| abstract_inverted_index.datasets. | 65 |
| abstract_inverted_index.diameter. | 24, 174 |
| abstract_inverted_index.frequency | 119 |
| abstract_inverted_index.published | 64 |
| abstract_inverted_index.resulting | 46, 104 |
| abstract_inverted_index.sharpness | 141 |
| abstract_inverted_index.wavefront | 193 |
| abstract_inverted_index.aberration | 40 |
| abstract_inverted_index.calculated | 116 |
| abstract_inverted_index.clinically | 126 |
| abstract_inverted_index.correction | 33 |
| abstract_inverted_index.correlated | 156 |
| abstract_inverted_index.individual | 12, 14, 18 |
| abstract_inverted_index.predictive | 158 |
| abstract_inverted_index.refractive | 32 |
| abstract_inverted_index.simulating | 78 |
| abstract_inverted_index.conditions. | 87 |
| abstract_inverted_index.correction, | 187 |
| abstract_inverted_index.correction. | 214 |
| abstract_inverted_index.corrections | 200 |
| abstract_inverted_index.development | 34 |
| abstract_inverted_index.experienced | 41 |
| abstract_inverted_index.experiments | 107, 133 |
| abstract_inverted_index.independent | 171 |
| abstract_inverted_index.keratoconic | 59 |
| abstract_inverted_index.objectively | 184, 201 |
| abstract_inverted_index.significant | 127 |
| abstract_inverted_index.corrections, | 195 |
| abstract_inverted_index.demonstrated | 108, 135 |
| abstract_inverted_index.dispensation | 211 |
| abstract_inverted_index.experiments, | 53 |
| abstract_inverted_index.performance. | 48 |
| abstract_inverted_index.relationship | 37 |
| abstract_inverted_index.computationally | 73, 92 |
| abstract_inverted_index.sphero-cylindrical | 186 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5036122103 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I44461941 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.01759121 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |