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

Abstract

BackgroundThe 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.ResultsFractal 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.ConclusionsOur 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.

Investigating Tissue Optical Properties and Texture Descriptors of the Retina in Patients with Multiple Sclerosis

“…The outer retina showed no significant differences between the groups which was in contrast with our earlier report showing significant fractal dimension changes in diabetic patients where, similarly, neurodegeneration was a proposed mechanism in the background of the observed changes. [ 54 ] This could point to the fact of a different disease mechanism at the level of the photoreceptors.…”

Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema

“…We expect that these parameters will provide valuable prognostic information to guide treatment decisions. However, despite the development of automated retinal layer segmentation methods applicable to normal eyes or eyes with limited pathological deformation or loss of retinal structures [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], most DME studies require either manual [22,[24][25][26]43] or semi-automatic [44] evaluation of OCT images. Currently few automated algorithms exist to quantify morphological or pathological features on images with DME [43,[45][46][47][48][49][50][51].…”

The Use of Optical Coherence Tomography for the Detection of Early Diabetic Retinopathy

“…In a study involving TD-OCT data of 74 healthy eyes, 38 eyes with T1DM with no retinopathy and 43 eyes with mild NPDR (MDR), the layer index measures were significantly smaller for all layers except the retinal pigment epithelium (RPE) in MDR eyes compared with healthy and DM eyes [47]. In the same study population, the assessment of fractal dimension of the GCL + IPL complex, OPL, and OS provided a more sensitive indicator than layer thickness alone for the discrimination of MDR eyes from healthy controls [48,49].…”