Automated Choroidal Segmentation in Enhanced Depth Imaging Optical Coherence Tomography Images

Document Type : Original Article (s)

Authors

1 MSC Student, Department of Biomedical Engineering, School of Medicine AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

2 PhD Student, Department of Biomedical Engineering, School of Medicine AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

3 Associate Professor, Department of Biomedical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

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.

Keywords

References

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Journal of Isfahan Medical School
Volume 31, Issue 230 - Serial Number 230
January and February 2013
Pages 343-350

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History

  • Receive Date: 17 February 2013
  • Revise Date: 01 November 2024
  • Accept Date: 06 April 2022

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