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Background: Early diagnosis of diseases is an important step in their treatment process, but it is difficult due to lack of satisfactorily sensitive imaging devices and difference in the parameters of the subjects. Asymmetry analysis of retinal nerve fiber layer thickness and total retinal thickness can provide a criterion for physicians to accomplish early diagnosis. Methods: Data were collected from 50 normal subjects (aged 35 ± 10 years) by a Topcon model of 3D-OCT 1000. For this purpose, a pipeline of procedures was utilized; segmentation of retinal layers by diffusion map method, scaling operator, automatic localization of the center of macula, and calculation of mean and SD. Findings: The maximum of average RNFL and total retina thickness in right and left eyes is seen in the perifoveal nasal, and the minimum is seen in the fovea. The results demonstrate an overall symmetry between the two eyes (P > 0.05). Tolerance limits in RNFL of normal subjects for 9 sectors (from 1 to 9), respectively, are 1.16 ± 0.99, 2.43 ± 1.57, 3.96 ± 2.08, 1.11 ± 1.09, 2.83 ± 1.66, 3.09 ± 1.39, 4.91 ± 1.79, 0.82 ± 1.25, and 3.31 ± 1.63 μm. Tolerance limits in total retina of normal subjects for 9 sectors, respectively, are 9.84 ± 2.75, 3.60 ± 2.74, 4.82 ± 3.23, 3.33 ± 1.76, 6.51 ± 3.69, 3.23 ± 1.30, 5.96 ± 2.62, 2.79 ± 1.42, 6.72 ± 2.57 μm. Conclusion: Asymmetry analysis can be a criterion for early detection of diseases. In this study asymmetry analysis and tolerance limits were calculated. [ABSTRACT FROM AUTHOR]

Background: Enhanced depth imaging optical coherence tomography images (EDI-OCT) is used for detailed imaging of the choroid layer that contains the highest amount of blood flow in the eye and is affected in several diseases such as choroidal polyps, age-related degeneration and central serous chorioretinopathy. Choroidal segmentation is really important, but the manual segmentation is time consuming and encounters difficulties when large numbers of data is available. Since a large amount of information is available in the images, non-automated and visual analysis of data is almost impossible for the ophthalmologist. The main goal of automatic segmentation was to help the ophthalmologists in the diagnosis and monitoring diseases related to the eye. Methods: The data used in this project was obtained from the Heidelberg OCT-HRA2-KT instrument. Fifty 2 dimensional data were used to evaluate the algorithm. In this study, the retinal pigment epithelium (RPE) and choroid was segmented using a boundary detection algorithm named dynamic programming. Findings: The proposed algorithm was compared with the manual segmentation and the results showed an unsigned error of 1.71 ± 0.93 pixels for retinal pigmented epithelium (RPE) extraction and 10.48 ± 4.11 pixels for choroid detection. It showed significant improvements over other approaches like k-means method. Conclusion: A few automated methods are applied in the choroid segmentation and most of the studies were mainly focused on the manual separation. In this study, a fast and automated method was provided for the segmentation of choroid area. [ABSTRACT FROM AUTHOR]