title: Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage.
abstract: 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.
authors.forename: Gábor Márk
authors.initials: GM
authors.lastname: Somfai
authors.forename: Erika
authors.initials: E
authors.lastname: Tátrai
authors.forename: Lenke
authors.initials: L
authors.lastname: Laurik
authors.forename: Boglárka E
authors.initials: BE
authors.lastname: Varga
authors.forename: Vera
authors.initials: V
authors.lastname: Ölvedy
authors.forename: William E
authors.initials: WE
authors.lastname: Smiddy
authors.forename: Robert
authors.initials: R
authors.lastname: Tchitnga
authors.forename: Anikó
authors.initials: A
authors.lastname: Somogyi
authors.affiliation: Miller School of Medicine, Bascom Palmer Eye Institute, University of Miami, Miami, Florida 33136, USA. dcabrera2@med.miami.edu.
authors.forename: Delia Cabrera
authors.initials: DC
authors.lastname: DeBuc
mesh_terms: D000328:Adult; D000465:Algorithms; D016022:Case-Control Studies; D003930:Diabetic Retinopathy / Q000175:diagnosis / Q000473:pathology; D042241:Early Diagnosis; D005260:Female; D017709:Fractals; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D012372:ROC Curve; D012160:Retina / Q000473:pathology*; D041623:Tomography, Optical Coherence / Q000379:methods*; D058067:Wavelet Analysis