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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

ROBERT F. COOPER ,
MARCO LOMBARDO ,
JOSEPH CARROLL ,
KENNETH R. SLOAN  and
GIUSEPPE LOMBARDO
ROBERT F. COOPER
Affiliation:
Department of Biomedical Engineering, Marquette University, 53223 Milwaukee, Wisconsin
MARCO LOMBARDO
Affiliation:
Fondazione G.B. Bietti IRCCS, 00198 Rome, Italy
JOSEPH CARROLL
Affiliation:
Department of Ophthalmology, Medical College of Wisconsin, 53226 Milwaukee, Wisconsin Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, 53226 Milwaukee, Wisconsin
KENNETH R. SLOAN
Affiliation:
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
GIUSEPPE LOMBARDO*
Affiliation:
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: [email protected]
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Abstract

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.

Keywords

Adaptive opticsAnisotropyCone photoreceptorsCone metricsCone orientation
Type
Research Article
Information
Visual Neuroscience , Volume 33 , 2016 , E005
Copyright
Copyright © Cambridge University Press 2016 

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References

Ahnelt, P.K. & Kolb, H. (2000). The mammalian photoreceptor mosaic-adaptive design. Progress in Retinal and Eye Research 19, 711–777.Google Scholar
Baraas, R.C., Carroll, J., Gunther, K.L., Chung, M., Williams, D.R., Foster, D.H. & Neitz, M. (2007). Adaptive optics retinal imaging reveals S-cone dystrophy in tritan color-vision deficiency. Journal of the Optical Society of America A 24, 1438–1447.Google Scholar
Carroll, J., Rossi, E.A., Porter, J., Neitz, J., Roorda, A., Williams, D.R., Neitz, M. (2010). Deletion of the X-linked opsin gene array locus control region (LCR) results in disruption of the cone mosaic. Vision Research 50, 1989–1999.Google Scholar
Carroll, J., Kay, D.B., Scoles, D., Dubra, A. & Lombardo, M. (2013). Adaptive optics retinal imaging—Clinical opportunities and challenges. Current Eye Research 38, 709–721.Google Scholar
Chiu, S.J., Lokhnygina, Y., Dubis, A.M., Dubra, A., Carroll, J., Izatt, J.A. & Farsiu, S. (2013). Automatic cone photoreceptor segmentation using graph theory and dynamic programming. Biomedical Optics Express 4, 924–937.Google Scholar
Chui, T.Y.P., Song, H. & Burns, S.A. (2008). Adaptive-optics imaging of human cone photoreceptor distribution. Journal of the Optical Society of America A 25, 3021–3029.Google Scholar
Coletta, N.J. & Williams, D.R. (1987). Psychophysical estimate of extrafoveal cone spacing. Journal of the Optical Society of America A 4, 1503–1513.Google Scholar
Cooper, R.F., Dubis, A.M., Pavaskar, A., Rha, J., Dubra, A. & Carroll, J. (2011). Spatial and temporal variation of rod photoreceptor reflectance in the human retina. Biomedical Optics Express 2, 2577–2589.Google Scholar
Curcio, C.A. & Sloan, K.R. (1992). Packing geometry of human cone photoreceptors: Variation with eccentricity and evidence for local anisotropy. Visual Neuroscience 9, 169–180.Google Scholar
Deans, S.R. (1983). The Radon Transform and Some of its Applications. New York: John Wiley & Sons.Google Scholar
Dees, E.W., Dubra, A. & Baraas, R.C. (2011). Variability in parafoveal cone mosaic in normal trichromatic individuals. Biomedical Optics Express 2, 1351–1358.Google Scholar
Dubra, A. & Sulai, Y. (2011). Reflective afocal broadband adaptive optics scanning ophthalmoscope. Biomedical Optics Express 2, 1757–1768.Google Scholar
Dubra, A., Sulai, Y., Norris, J.L., Cooper, R.F., Dubis, A.M., Williams, D.R. & Carroll, J. (2011). Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope. Biomedical Optics Express 2, 1864–1876.Google Scholar
Garrioch, R., Langlo, C., Dubis, A.M., Cooper, R.F., Dubra, A. & Carroll, J. (2012). Repeatability of in vivo parafoveal cone density and spacing measurements. Optometry and Vision Science 89, 632–643.Google Scholar
Herman, G. & Kuba, A. (1999). Discrete Tomography. Foundations, Algorithms, and Applications. New York, NY: Springer Science+Business Media.Google Scholar
Hirsch, J. & Miller, W.H. (1987). Does cone positional disorder limit resolution. Journal of the Optical Society of America A 4, 1481–1492.Google Scholar
Li, K.Y. & Roorda, A. (2007). Automated identification of cone photoreceptors in adaptive optics retinal images. Journal of the Optical Society of America A 24, 1358–1363.Google Scholar
Li, K.Y., Tiruveedhula, P. & Roorda, A. (2010). Intersubject variability of foveal cone photoreceptor density in relation to eye length. Investigative Ophthalmology & Visual Science 51, 6858–6867.Google Scholar
Lombardo, M., Lombardo, G., Schiano Lomoriello, D., Ducoli, P., Stirpe, M. & Serrao, S. (2013a). Interocular symmetry of parafoveal photoreceptor cone density. Retina 33, 1640–1649.Google Scholar
Lombardo, M., Serrao, S., Ducoli, P. & Lombardo, G. (2013b). Eccentricity dependent changes of density, spacing and packing arrangement of parafoveal cones. Ophthalmic & Physiological Optics 33, 516–526.Google Scholar
Lombardo, M., Serrao, S., Ducoli, P. & Lombardo, G. (2013c). Influence of sampling window size and orientation on parafoveal cone packing density. Biomedical Optics Express 4, 1318–1331.Google Scholar
Lombardo, M., Serrao, S. & Lombardo, G. (2014). Technical factors influencing cone packing density estimates in adaptive optics flood illuminated retinal images. PLoS One 9, e107402.CrossRefGoogle ScholarPubMed
Meitav, N. & Ribak, E.N. (2011). Measuring the ordering of closely-packed particles. Applied Physics Letters 99, 1–3.CrossRefGoogle Scholar
Park, S.P., Chung, J.K., Greenstein, V., Tsang, S.H. & Chang, S. (2013). A study of factors affecting the human cone photoreceptor density measured by adaptive optics scanning laser ophthalmoscope. Experimental Eye Research 108, 1–9.CrossRefGoogle ScholarPubMed
Pum, D., Ahnelt, P.K. & Grasl, M. (1990). Iso-orientation areas in the foveal cone mosaic. Visual Neuroscience 5, 511–523.Google Scholar
Ratnam, K., Västinsalo, H., Roorda, A., Sankila, E-M.K. & Duncan, J.L. (2013). Cone structure in patients with Usher syndrome type III and mutations in the Clarin 1 gene. JAMA Ophthalmology 131, 67–74.Google Scholar
Rossi, E.A., Chung, M., Dubra, A., Hunter, J.J., Merigan, W.H. & Williams, D.R. (2011). Imaging retinal mosaics in the living eye. Eye 25, 301–308.Google Scholar
Song, H., Chui, T.Y., Zhong, Z., Elsner, A.E. & Burns, S.A. (2011). Variation of cone photoreceptor packing density with retinal eccentricity and age. Investigative Ophthalmology and Visual Science 52, 7376–7384.Google Scholar
Stillinger, F.H. Jr., DiMarzio, E.A. & Kornegay, R.L. (1964). Systematic approach to explanation of the rigid disk phase transition. Journal of Chemical Physics 40, 1564–1576.Google Scholar
Wagner-Schuman, M., Neitz, J., Rha, J., Williams, D.R., Neitz, M. & Carroll, J. (2010). Color-deficient cone mosaics associated with Xq28 opsin mutations: A stop codon versus gene deletions. Vision Research 50, 2396–2402.Google Scholar
Wojtas, D.H., Wu, B., Ahnelt, P.K., Bones, P.J. & Millane, R.P. (2008). Automated analysis of differential interference contrast microscopy images of the foveal cone mosaic. Journal of the Optical Society of America A 25, 1181–1189.Google Scholar

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