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Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images

Published online by Cambridge University Press:  22 March 2016

Department of Biomedical Engineering, Marquette University, 53223 Milwaukee, Wisconsin
Fondazione G.B. Bietti IRCCS, 00198 Rome, Italy
Department of Ophthalmology, Medical College of Wisconsin, 53226 Milwaukee, Wisconsin Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, 53226 Milwaukee, Wisconsin
Department of Computer and Information Sciences, University of Alabama at Birmingham, 35294 Birmingham, Alabama Department of Ophthalmology, School of Medicine, University of Alabama at Birmingham, 35294 Birmingham, Alabama
Consiglio Nazionale delle Ricerche, Istituto per i Processi Chimico-Fisici (CNR-IPCF), 98158 Messina, Italy Vision Engineering Italy srl, 00198 Rome, Italy
*Address correspondence to: Giuseppe Lombardo, Consiglio Nazionale delle Ricerche, Istituto per i Processi Chimico-Fisici (CNR-IPCF), Viale Stagno D'Alcontres 37, 98158 Messina, Italy. E-mail:


The ability to noninvasively image the cone photoreceptor mosaic holds significant potential as a diagnostic for retinal disease. Central to the realization of this potential is the development of sensitive metrics for characterizing the organization of the mosaic. Here we evaluated previously-described and newly-developed (Fourier- and Radon-based) methods of measuring cone orientation in simulated and real images of the parafoveal cone mosaic. The proposed algorithms correlated well across both simulated and real mosaics, suggesting that each algorithm provides an accurate description of photoreceptor orientation. Despite high agreement between algorithms, each performed differently in response to image intensity variation and cone coordinate jitter. The integration property of the Fourier transform allowed the Fourier-based method to be resistant to cone coordinate jitter and perform the most robustly of all three algorithms. Conversely, when there is good image quality but unreliable cone identification, the Radon algorithm performed best. Finally, in cases where the cone coordinate reliability was excellent, the method previously described by Pum and colleagues performed best. These descriptors are complementary to conventional descriptive metrics of the cone mosaic, such as cell density and spacing, and have the potential to aid in the detection of photoreceptor pathology.

Research Article
Copyright © Cambridge University Press 2016 

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