Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage

BMC Bioinformatics

Gábor Márk Somfai³  Erika Tátrai³  Lenke Laurik³  Boglárka E Varga³  Vera Ölvedy³  William E Smiddy¹  Robert Tchitnga²  Anikó Somogyi⁴  Delia Cabrera DeBuc¹ 

[1] Miller School of Medicine, Bascom Palmer Eye Institute, University of Miami, Miami, Florida 33136, USA

[2] Faculty of Science, Department of Physics, University of Dschang, Dschang, Cameroon

[3] Department of Ophthalmology, Faculty of Medicine Semmelweis University, Budapest, Hungary

[4] 2nd Department of Internal Medicine, Faculty of Medicine Semmelweis University, Budapest, Hungary

关键词: Ophthalmology;    Diabetic retinopathy;    Wavelet algorithm;    Fractal dimension;    Fractal analysis;    Optical coherence tomography;   

Others  :  1086139
DOI  :  10.1186/1471-2105-15-295

 received in 2013-03-21, accepted in 2014-06-18,  发布年份 2014

【 摘 要 】

Background

The sensitivity of Optical Coherence Tomography (OCT) images to identify retinal tissue morphology characterized by early neural loss from normal healthy eyes is tested by calculating structural information and fractal dimension. OCT data from 74 healthy eyes and 43 eyes with type 1 diabetes mellitus with mild diabetic retinopathy (MDR) on biomicroscopy was analyzed using a custom-built algorithm (OCTRIMA) to measure locally the intraretinal layer thickness. A power spectrum method was used to calculate the fractal dimension in intraretinal regions of interest identified in the images. ANOVA followed by Newman-Keuls post-hoc analyses were used to test for differences between pathological and normal groups. A modified p value of <0.001 was considered statistically significant. Receiver operating characteristic (ROC) curves were constructed to describe the ability of each parameter to discriminate between eyes of pathological patients and normal healthy eyes.

Results

Fractal dimension was higher for all the layers (except the GCL + IPL and INL) in MDR eyes compared to normal healthy eyes. When comparing MDR with normal healthy eyes, the highest AUROC values estimated for the fractal dimension were observed for GCL + IPL and INL. The maximum discrimination value for fractal dimension of 0.96 (standard error =0.025) for the GCL + IPL complex was obtained at a FD ≤ 1.66 (cut off point, asymptotic 95% Confidence Interval: lower-upper bound = 0.905-1.002). Moreover, the highest AUROC values estimated for the thickness measurements were observed for the OPL, GCL + IPL and OS. Particularly, when comparing MDR eyes with control healthy eyes, we found that the fractal dimension of the GCL + IPL complex was significantly better at diagnosing early DR, compared to the standard thickness measurement.

Conclusions

Our results suggest that the GCL + IPL complex, OPL and OS are more susceptible to initial damage when comparing MDR with control healthy eyes. Fractal analysis provided a better sensitivity, offering a potential diagnostic predictor for detecting early neurodegeneration in the retina.

【 授权许可】

   
2014 Somfai et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, Hee MR, Flotte T, Gregory K, Puliafito CA, Fujimoto JG: Optical coherence tomography. Science 1991, 254(5035):1178-1181.
• [2]Cabrera DeBuc D: A review of algorithms for segmentation of retinal image data using optical coherence tomography. Pei-Gee Ho INTECH: In Image Segmentation. Edited by Dr; 2011.
• [3]Cabrera Fernandez D, Salinas HM, Puliafito CA: Automated detection of retinal layer structures on optical coherence tomography images. Opt Express 2005, 13(25):10200-10216.
• [4]Debuc DC, Somfai GM: Early detection of retinal thickness changes in diabetes using optical coherence tomography. Med Sci Monitor 2010, 16(3):Mt15-Mt21.
• [5]Tatrai E, Simo M, Iljicsov A, Nemeth J, Debuc DC, Somfai GM: In vivo evaluation of retinal neurodegeneration in patients with multiple sclerosis. PLoS One 2012, 7(1):e30922.
• [6]Gossage KW, Tkaczyk TS, Rodriguez JJ, Barton JK: Texture analysis of optical coherence tomography images: feasibility for tissue classification. J Biomed Opt 2003, 8(3):570-575.
• [7]Gao W, Tátrai E, Ölvedy V, Varga B, Laurik L, Somogyi A, Somfai GM, DeBuc DC: Investigation of changes in thickness and reflectivity from layered retinal structures of healthy and diabetic eyes with optical coherence tomography. Hemoglobin 2011, 100(100):91.
• [8]Gao W, Tatrai E, Somfai GM, DeBuc D: Assessing the performance of optical properties determination of intraretinal layers in healthy normal and type 1 diabetic eyes using optical coherence tomography. Invest Ophthalmol Vis Sci 2011, 52(6):3689.
• [9]Gao W, Tatrai E, Somfai GM, Cabrera DeBuc D: Evaluation of intraretinal scattering measurements in eyes of healthy and type 1 diabetic subjects using optical coherence tomography. Invest Ophthalmol Vis Sci 2010, 51(5):1786.
• [10]Mandelbrot BB: Fractals : form, chance, and dimension. San Francisco: W. H. Freeman; 1977.
• [11]Nailon WH: Texture analysis methods for medical image characterisation. In Biomedical Imaging. Edited by Mao Y. (Rijeka): InTec; 2010:75-100.
• [12]Daxer A: Characterisation of the neovascularisation process in diabetic retinopathy by means of fractal geometry: diagnostic implications. Albrecht Von Graefes Arch Klin Exp Ophthalmol 1993, 231(12):681-686.
• [13]Daxer A: The fractal geometry of proliferative diabetic retinopathy: implications for the diagnosis and the process of retinal vasculogenesis. Curr Eye Res 1993, 12(12):1103-1109.
• [14]Avakian A, Kalina RE, Sage EH, Rambhia AH, Elliott KE, Chuang EL, Clark JI, Hwang JN, Parsons-Wingerter P: Fractal analysis of region-based vascular change in the normal and non-proliferative diabetic retina. Curr Eye Res 2002, 24(4):274-280.
• [15]Landini G, Murray PI, Misson GP: Local connected fractal dimensions and lacunarity analyses of 60 degrees fluorescein angiograms. Invest Ophthalmol Vis Sci 1995, 36(13):2749-2755.
• [16]Cabrera DeBuc D, Tchitnga R: Photoreceptor rearrangement and vision restoration in eyes with outer retinopathy: Quantitative assessment by fractal analysis. Pennsylvania: APS March Meeting Pittsburgh; 2009.
• [17]Kim P, Iftekharuddin K, Gunvant P, Tóth M, Holló G, Essock E: Efficacy of fractal analysis in identifying glaucomatous damage. In Pro. SPIE 7627, Medical Imaging. Image Perception, Observer Performance, and Technology Assessment, 76271G; 2010. (February 23, 2010); doi:10.1117/12.848428
• [18]Schmoll T, Singh AS, Blatter C, Schriefl S, Ahlers C, Schmidt-Erfurth U, Leitgeb RA: Imaging of the parafoveal capillary network and its integrity analysis using fractal dimension. Biomedical optics express 2011, 2(5):1159-1168.
• [19]Diabetic Retinopathy Clinical Research N: Diabetic retinopathy study. Report Number 7. A modification of the Airlie House classification of diabetic retinopathy. Invest Ophthalmol Vis Sci 1981, 21(Pt 2):210-226.
• [20]Group ETDRSR: Photocoagulation for diabetic macular edema. Early Treatment Diabetic Retinopathy Study report number 1. Arch Ophthalmol 1985, 103(12):1796-1806.
• [21]Wilkinson CP, Ferris FL 3rd, Klein RE, Lee PP, Agardh CD, Davis M, Dills D, Kampik A, Pararajasegaram R, Verdaguer JT: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 2003, 110(9):1677-1682.
• [22]Hasegawa M, Liu J, Okuda K, Nunobiki M: Calculation of the fractal dimensions of machined surface profiles. Wear 1996, 192(1):40-45.
• [23]Debuc DC, Salinas HM, Ranganathan S, Tatrai E, Gao W, Shen M, Wang J, Somfai GM, Puliafito CA: Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis. J Biomed Opt 2010, 15(4):046015.
• [24]DeBuc DC, Somfai GM, Ranganathan S, Tatrai E, Ferencz M, Puliafito CA: Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software. J Biomed Opt 2009, 14(6):064023.
• [25]Cabrera DeBuc D, Gao W, Tatrai E, Laurik L, Varga B, Olvedy V, Smiddy W, Tchintga R, Somogyi A, Somfai G: Extracting Diagnostic Information from Optical Coherence Tomography Images of Diabetic Retinal Tissues Using Depth-dependent Attenuation Rate and Fractal Analysis. In Biomedical Optics. Proceedings of SPIE, Miami, Florida United States; 01/2012, http://dx.doi.org/10.1364/BIOMED.2012.BTu3A.74: Optical Society of America; 2012.
• [26]Salinas HM, Fernandez DC: Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising optical coherence. IEEE transaction on medical imaing 2007, 26(6):761-771.
• [27]Wehbe H, Ruggeri M, Jiao S, Gregori G, Puliafito CA, Zhao W: Automatic retinal blood flow calculation using spectral domain optical coherence tomography. Opt Express 2007, 15(23):15193-15206.
• [28]Castelli C, Losa GA: Ultrastructural complexity of nuclear components during early apoptotic phases in breast cancer cells. Anal Cell Pathol 2001, 23(1):1-9.
• [29]van Dijk HW, Verbraak FD, Kok PH, Garvin MK, Sonka M, Lee K, Devries JH, Michels RP, van Velthoven ME, Schlingemann RO, Abramoff MD: Decreased retinal ganglion cell layer thickness in patients with type 1 diabetes. Invest Ophthalmol Vis Sci 2010, 51(7):3660-3665.
• [30]van Dijk HW, Verbraak FD, Kok PH, Stehouwer M, Garvin MK, Sonka M, DeVries JH, Schlingemann RO, Abramoff MD: Early neurodegeneration in the retina of type 2 diabetic patients. Invest Ophthalmol Vis Sci 2012, 53(6):2715-2719.
• [31]Lipsitz LA, Goldberger AL: Loss of ‘complexity’ and aging. JAMA 1992, 267(13):1806-1809.
• [32]Goldberger AL, Amaral LA, Hausdorff JM, Ivanov P, Peng CK, Stanley HE: Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A 2002, 99(Suppl 1):2466-2472.
• [33]Family F, Masters BR, Platt DE: Fractal pattern formation in human retinal vessels. Physica D: Nonlinear Phenomena 1989, 38(1):98-103.
• [34]Masters BR: Fractal analysis of the vascular tree in the human retina. Annu Rev Biomed Eng 2004, 6:427-452.
• [35]Liew G, Wang JJ, Mitchell P, Wong TY: Retinal vascular imaging: a new tool in microvascular disease research. Circulation Cardiovascular imaging 2008, 1(2):156-161.
• [36]Azemin MZC, Kumar DK, Wong TY, Wang JJ, Mitchell P, Kawasaki R, Wu H: Age-related rarefaction in the fractal dimension of retinal vessel. Neurobiol Aging 2012, 33(1):194. e191-194. e194.
• [37]Somfai GM, Tátrai E, Laurik L, Varga B, Ölvedy V, Jiang H, Wang J, Smiddy WE, Somogyi A, DeBuc DC: Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes. BMC Bioinformatics 2014, 15:106. doi:10.1186/1471-2105-15-106 BioMed Central Full Text
• [38]Faber D, van der Meer F, Aalders M, van Leeuwen T: Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography. Opt Express 2004, 12(19):4353-4365.
• [39]Popescu DP, Flueraru C, Mao Y, Chang S, Sowa MG: Signal attenuation and box-counting fractal analysis of optical coherence tomography images of arterial tissue. Biomedical optics express 2010, 1(1):268-277.
• [40]Kestener P, Arneodo A: Three-dimensional wavelet-based multifractal method: the need for revisiting the multifractal description of turbulence dissipation data. Phys Rev Lett 2003, 91(19):194501.
• [41]Veneziano D, Moglen G, Bras R: Multifractal analysis: pitfalls of standard procedures and alternatives. Phys Rev E 1993, 52(2):1387-1398.
• [42]Wang Y, Jiang H, Shen M, Lam BL, DeBuc DC, Ye Y, Li M, Tao A, Shao Y, Wang J: Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography. J Biomed Opt 2012, 17(6):066013.