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

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

Background and Purpose Methods: Vessel Segmentation Methods: Optic Nerve Head Localization Experiments and Results

Segmentation and localization of retinal structures Our vessel segmentation[2] is a multiscale method us-The optic nerve head is localized by a modified FRST. Each method was tested on 45 images (resolution: is an essential pre-processing step for many applica-ing the vesselness feature (see Fig. 2): Our modifications[3] are the following: 3504 × 2336 pixels) of the public available high res-tions of fully automatic or computer aided medical diagnosis. In this work, we propose a framework for localizing and segmenting the most important retinal structures in color fundus images: • vascular tree • optic nerve head 1. Histogram stretching and denoising using bilateral filter 2. Iterative down sampling: •Highest resolution is the input resolution •Further lower resolution images are obtained by rescaling the last image with a factor 0.5 3. Vesselness extraction in each image 1.Denoising and elimination of small vessels from the image using median filtering 2.Upper-bound constraint introduced to the gradient in the accumulator map to neglect edges of vessels 3.Global maximum selection over all maxima at each map to estimate ONH diameter olution fundus (HRF) database (www5.informatik.unierlangen.de/research/data/fundus-images/), and the results are compared to a manually generated gold standard: 1.Vessel segmentation accuracy

Evaluation of optical features of the macula in multiple sclerosis

Purpose Differences in optical and irregularity measures of abnormal retinal tissue may provide additional information of impairment of the retinal nerve fiber layer (RNFL) and ganglion cell/inner plexiform layer complex (GCL+IPL) in multiple sclerosis (MS). The purpose of our study was to estimate these retinal changes in MS. Methods Twenty‐seven patients with MS were examined using Stratus OCT. The raw macular OCT data was exported and processed using OCTRIMA software and the fractal dimension (FD) and layer index (LI) values of seven intraretinal layers were obtained. The enrolled eyes were divided into two groups, based on ON in the history (ON+ group, n=13 and ON‐ group, n=14). Data of 73 healthy subjects (N) were used as controls. ANOVA with Newman‐Keuls post‐hoc analysis was used for the comparison of FD and LI values. The level of significance was set at p<0.001. Results A significant decrease was observed in LI in the entire macula and the perifoveal region in RNFL (12.4±1.4, 10.8±0.8, 9.3 ±1.4 and 14.0±1.7, 12.0±0.9, 10.0±1.7 for the N, ON‐ and ON+ groups, respectively). The RNFL in the ON‐ group was significantly different from both the N and ON+ groups (p<0.001 for all comparisons). No significant changes were found in the other layers. A significant increase was observed between the ON‐ and ON+ groups in FD in the entire macula and the perifoveal region in the RNFL layer (1.8±0.04, 1.8±0.07 p<0.001 and 1.6±0.06, 1.6±0.1 p<0.001, respectively) but no significant changes were found in the other layers. Conclusion In MS, the optical features of the ganglion cells and the RNFL also change besides the pathological remodeling of macular tissue. This result may help to improve the diagnostic efficacy of OCT in MS. Commercial interes