Two-dimensional fractal analysis of retinal tissue of healthy and diabetic eyes with optical coherence tomography
Paper #3114 received 2016.11.09; accepted for publication 2016.12.30; published online 2016.12.31.
In the ophthalmic research, the measurement of the retinal thickness is usually employed for characterizing the structural changes of the retinal tissue. However, changes in the fractal dimension (FD) may provide additional information regarding the structure of the retinal layers and their early damage in ocular diseases. In the present paper, we investigated the possibility of detecting changes in the structure of the cellular layers of the retina by applying a two-dimensional fractal analysis to optical coherence tomography (OCT) images. OCT images were obtained from diabetic patients without retinopathy (DM, n = 38 eyes) and with mild diabetic retinopathy (MDR, n = 43 eyes) as well as in healthy subjects (Controls, n = 74 eyes). The two-dimensional fractal dimension was calculated using the differentiate box counting methodology. We evaluated the usefulness of quantifying the fractal dimension of layered structures in the detection of retinal damage. Generalized estimating equations considering within-subject inter-eye relations were used to test for differences between the groups. An adjusted p-value of <0.001 was considered statistically significant. Receiver operating characteristic (ROC) curves were constructed to describe the ability of the fractal dimension to discriminate between the eyes of DM, MDR, and healthy eyes. Lower values of the fractal dimension were observed in all layers in the MDR eyes compared with controls except in the inner nuclear layer (INL). Lower values of the fractal dimension were also found in all layers in the MDR eyes compared with DM eyes. The highest area under receiver operating characteristic curve (AUROC) values estimated for the fractal dimension were observed for the outer plexiform layer (OPL) and outer segment photoreceptors (OS) when comparing MDR eyes with controls. The highest AUROC value estimated for the fractal dimension were also observed for the retinal nerve fiber layer (RNFL) and OS when comparing MDR eyes with DM eyes. Our results suggest that fractal dimension of the intraretinal layers may provide useful information to differentiate pathological from healthy eyes. Further research is warranted to determine how this approach may be used to aid diagnosis of retinal neurodegeneration at the early stage.
1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178-1181 (1991).
2. D. C. DeBuc, and G. M. Somfai, “Early detection of retinal thickness changes in diabetes using optical coherence tomography,” Medical Science Monitor 16(3), Mt15-21 (2010).
3. E. Tatrai, M. Simo, A. IIjicsov, J. Nemeth, D. C. DeBuc, and G. M. Somfai, “In vivo evaluation of retinal neurodegeneration in patients with multiple sclerosis,” PLoS One 7(1) e30922 (2012).
4. D. C. DeBuc, “Novel methods and diagnostic tools in diabetic retinopathy,” Retinal Physician 12, 22-27 (2015).
5. G. M. Somfai, E. Tatrai, L. Laurik, B. E. Varga, V. Olvedy, H. Jiang, J. H. Wang, W. E. Smiddy, A. Somogyi, and D. C. DeBuc, “Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes,” BMC Bioinformatics 15(1), 106 (2014). Crossref
6. G. M. Somfai, E. Tatrai, L. Laurik, B. E. Varga, V. Olvedy, W. E. Smiddy, R. Tchitnga, A. Somogyi, and
7. D. C. DeBuc, “Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage,” BMC Bioinformatics 15, 295 (2014).
8. D. C. DeBuc, E. Tatrai, L. Laurik, B. E. Varga, V. Olvedy, A. Somogyi, W. E. Smiddy, and G. M. Somfai, “Identifying local structural and optical derangement in the neural retina of individuals with type 1 diabetes,” Journal of Clinical and Experimental Ophthalmology 4(4) 1000289 (2013). Crossref
9. U. H. Schaudig, C. Glaefke, F. Scholz, and G. Richard, “Optical coherence tomography for retinal thickness measurement in diabetic patients without clinically significant macular edema,” Ophthalmic Surgery Lasers 31(3), 182-186 (2000).
10. T. Oshitari, K. Hanawa, and E. Adachi-Usami, “Changes of macular and RNFL thicknesses measured by Stratus OCT in patients with early stage diabetes,” Eye (London) 23(4), 884-889 (2009). Crossref
11. B. Asefzadeh, B. M. Fisch, C. E. Parenteau, and A. A. Cavallerano, “Macular thickness and systemic markers for diabetes in individuals with no or mild diabetic retinopathy,” Clinical & Experimental Ophthalmology 36(5), 455-463 (2008). Crossref
12. W. Goebel, and T. Kretzchmar-Gross, “Retinal thickness in diabetic retinopathy: a study using optical coherence tomography (OCT),” Retina 22(6), 759-767 (2002).
13. N. M. Bressler, A. R. Edwards, A. N. Antoszyk, R. W. Beck, D. J. Browning, A. P. Ciardella, R. P. Danis,
14. M. J. Elman, S. M. Friedman, A. R. Glassman, J. G. Gross, H. K. Li, T. J. Murtha, T. W. Stone, and J. K. Sun, “Retinal thickness on Stratus optical coherence tomography in people with diabetes and minimal or no diabetic retinopathy,” American Journal of Ophthalmology 145(5), 894-901 (2008). Crossref
15. P. G. H. Clarke, “Developmental cell death: morphological diversity and multiple mechanisms,” Anatomy and Embryology 181(3) 195-213 (1990).
16. C. D. M. Fletcher (ed.), Diagnostic histopathology of tumors, 2nd. ed., Churchill Livingstone, London (2000). ISBN: 978-0-443-07992-4.
17. G. M. Somfai, H. M. Salinas, C. A. Puliafito, and D. C. Fernandez, “Evaluation of potential image acquisition pitfalls during optical coherence tomography and their influence on retinal image segmentation,” Journal of Biomedical Optics 12(4), 041209 (2007). Crossref
18. D. C. DeBuc, System and method for early detection of diabetic retinopathy using optical coherence tomography. U.S. Patent WO2010080576 (2010).
19. D. C. DeBuc, H. M. Salinas, and C. A. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Optics Express 13(25), 10200-10216 (2005). Crossref
20. C. Flueraru, D. P. Popescu, Y. Mao, S. Chang, and M. G. Sowa, “Added soft tissue contrast using signal attenuation and the fractal dimension for optical coherence tomography images of porcine arterial tissue,” Physics in Medicine and Biology 55(8), 2317-2331 (2010). Crossref
21. A. C. Sullivan, J. P. Hunt, and A. L. Oldenburg, “Fractal analysis for classification of breast carcinoma in optical coherence tomography,” Journal of Biomedical Optics 16(6), 066010 (2011). Crossref
22. W. Gao, Improving the quantitative assessment of intraretinal features by determining both structural and optical properties of the retinal tissue with optical coherence tomography, Ph.D thesis, University of Miami (2012).
23. M. Hasegawa, J. Liu, K. Okuda, and M. Nunobiki, “Calculation of the fractal dimensions of machined surface profiles,” Wear 192(1-2), 40-45 (1996).
24. N. Sarkar, and B. B. Chaudhuri, “An efficient approach to estimate fractal dimension of textural images,” Pattern Recognition 25(9), 1035-1041 (1992). Crossref
25. J. Li, Q. Du, and C. Sun, “An improved box-counting method for image fractal dimension estimation,” Pattern Recognition 42(11), 2460-2469 (2009). Crossref
26. A. J. Barber, E., Lieth, S. A. Khin, D. A. Antonetti, A. G. Buchanan, and T. W. Gardner, “Neural apoptosis in the retina during experimental and human diabetes. Early onset and effect of insulin,” The Journal of Clinical Investigation 102 (4), 783-791 (1998). Crossref
27. S. H. Park, J. W. Park, S. J. Park, K. Y. Kim, J. W. Chung, M. H. Chun, and S. J. Oh, “Apoptotic death of photoreceptors in the streptozotocin-induced diabetic rat retina,” Diabetologia 46(9), 1260-1268 (2003). Crossref
28. E. Rungger-Brandle, A. A. Dosso, and P. M. Leuenberger, “Glial reactivity, an early feature of diabetic retinopathy,” Investigative Ophthalmology and Visual Science 41(7), 1971-1980 (2000).
29. X. X. Zeng, Y. K. Ng, and E. A. Ling, “Neuronal and microglial response in the retina of streptozotocin- induced diabetic rats,” Visual Neuroscience 17(3), 463-471 (2000). Crossref
30. A. J. Barber, D. A. Antonetti, T. S. Kern, C. E. N. Reiter, R. S. Soans, J. K. Krady, S. W. Levison, T. W. Gardner, and S. K. Bronson, “The Ins2Akita mouse as a model of early retinal complications in diabetes,” Investigative Ophthalmology and Visual Science 46(6), 2210-2218 (2005). Crossref
31. B. J. Lujan, A. Roorda, J. A. Croskrey, A. M. Dubis, R. F. Cooper, J. K. Bayabo, J. L. Duncan, B. J. Antony, J. Caroll, “Directional optical coherence tomography provides accurate outer nuclear layer and Henle fiber layer measurements,” Retina 35(8), 1511-1520 (2015).
32. T. Otani, Y. Yamaguchi, and S. Kishi, “Improved visualization of Henle fiber layer by changing the measurement beam angle on optical coherence tomography,” Retina 31(3), 497-501 (2011).
33. R. Akshikar, M. Richardson, R. Crosby-Nwaobi, R., A. Abdelhay, S. Sivaprasad, S. Heng, “Retinal neuronal changes in people with diabetes,” Investigative Ophthalmology and Visual Science 53, 2852 (2012).
34. A. Verma, P. K. Rani, R. Raman, S. S. Pal, G. Laxmi, M. Gupta, C. Sahu, K. Vaitheeswaran, and T. Sharma, “Is neuronal dysfunction an early sign of diabetic retinopathy? Microperimetry and spectral domain optical coherence tomography (SD-OCT) study in individuals with diabetes, but no diabetic retinopathy,” Eye (London) 23(9), 1824-1830 (2009). Crossref
35. P. M. Martin, P. Roon, T. K. Van Ells, V. Ganapathy, and S. B. Smith, “Death of retinal neurons in streptozotocin-induced diabetic mice,” Investigative Ophthalmology and Visual Science 45(9), 3330-3336 (2004). Crossref
36. G. Liew, J. J. Wang, P. Mitchell, T. Y. Wong, “Retinal vascular imaging: a new tool in microvascular disease research,” Circulation Cardiovascular imaging 1(2), 156–161 (2008).
37. M. Hasegawa, J. Liu, K. Okuda, M. Nunobiki, “Calculation of the fractal dimensions of machined surface profiles,” Wear 192(1), 40–45 (1996). Crossref
© 2014-2019 Samara National Research University. All Rights Reserved.
Public Media Certificate (RUS). 12+