English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/29977
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Bayesian model of Snellen visual acuity

AuthorsNestares García, Óscar; Navarro, Rafael; Antona, Beatriz
KeywordsSnellen visual acuity (VA)
Vision tests
Issue DateJul-2003
PublisherOptical Society of America
CitationJournal of the Optical Society of America A 20(7):1371-81 (2003)
AbstractA Bayesian model of Snellen visual acuity (VA) has been developed that, as far as we know, is the first one that includes the three main stages of VA: (1) optical degradations, (2) neural image representation and contrast thresholding, and (3) character recognition. The retinal image of a Snellen test chart is obtained from experimental wave-aberration data. Then a subband image decomposition with a set of visual channels tuned to different spatial frequencies and orientations is applied to the retinal image, as in standard computational models of early cortical image representation. A neural threshold is applied to the contrast responses to include the effect of the neural contrast sensitivity. The resulting image representation is the base of a Bayesian pattern-recognition method robust to the presence of optical aberrations. The model is applied to images containing sets of letter optotypes at different scales, and the number of correct answers is obtained at each scale; the final output is the decimal Snellen VA. The model has no free parameters to adjust. The main input data are the eye’s optical aberrations, and standard values are used for all other parameters, including the Stiles–Crawford effect, visual channels, and neural contrast threshold, when no subject specific values are available. When aberrations are large, Snellen VA involving pattern recognition differs from grating acuity, which is based on a simpler detection (or orientation-discrimination) task and hence is basically unaffected by phase distortions introduced by the optical transfer function. A preliminary test of the model in one subject produced close agreement between actual measurements and predicted VA values. Two examples are also included: (1) application of the method to the prediction of the VA in refractive-surgery patients and (2) simulation of the VA attainable by correcting ocular aberrations.
DescriptionContiene: fórmulas y 7 ilustraciones.
Publisher version (URL)http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1371
Appears in Collections:(CFMAC-IO) Artículos
Files in This Item:
File Description SizeFormat 
EDB631F6-0BE7-0358-1E0A998629219C94_72942.pdf413,06 kBAdobe PDFThumbnail
Show full item record

Related articles:

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.