AUTOMATED CHOROIDAL SEGMENTATION IN ENHANCED DEPTH IMAGING OPTICAL COHERENCE TOMOGRAPHY IMAGES

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DANESH HAJAR; KAFIEH RAHELEH; RABBANI HOSSEIN

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Journal: JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S) Year:2013 | Volume:31 | Issue:230 Page(s): 343-350

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Title

AUTOMATED CHOROIDAL SEGMENTATION IN ENHANCED DEPTH IMAGING OPTICAL COHERENCE TOMOGRAPHY IMAGES

Author(s)

DANESH HAJAR | KAFIEH RAHELEH | RABBANI HOSSEIN | Issue Writer Certificate

Keywords

ENHANCED DEPTH IMAGING OPTICAL COHERENCE TOMOGRAPHY IMAGES (EDI-OCT)Q2

CHOROIDQ2

DYNAMIC PROGRAMMINGQ1

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.

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APA: Copy

DANESH, HAJAR, KAFIEH, RAHELEH, & RABBANI, HOSSEIN. (2013). AUTOMATED CHOROIDAL SEGMENTATION IN ENHANCED DEPTH IMAGING OPTICAL COHERENCE TOMOGRAPHY IMAGES. JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S), 31(230), 343-350. SID. https://sid.ir/paper/49595/en

Vancouver: Copy

DANESH HAJAR, KAFIEH RAHELEH, RABBANI HOSSEIN. AUTOMATED CHOROIDAL SEGMENTATION IN ENHANCED DEPTH IMAGING OPTICAL COHERENCE TOMOGRAPHY IMAGES. JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S)[Internet]. 2013;31(230):343-350. Available from: https://sid.ir/paper/49595/en

IEEE: Copy

HAJAR DANESH, RAHELEH KAFIEH, and HOSSEIN RABBANI, “AUTOMATED CHOROIDAL SEGMENTATION IN ENHANCED DEPTH IMAGING OPTICAL COHERENCE TOMOGRAPHY IMAGES,” JOURNAL OF ISFAHAN MEDICAL SCHOOL (I.U.M.S), vol. 31, no. 230, pp. 343–350, 2013, [Online]. Available: https://sid.ir/paper/49595/en

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