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2000
Volume 15, Issue 3
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Background: Optical Coherence Tomography (OCT) is a non-invasive medical imaging technique that provides high-resolution cross-sectional images of the retina. There is a need to develop algorithms for obtaining quantitative and qualitative information about the retina which are essential for assessing and managing eye conditions. Methods: This work emphasizes on an automated image processing algorithm for segmenting retinal layers. It involves preprocessing of the acquired retinal SD-OCT image (B-scan) using the proposed automatic Anisotropic diffusion filter, followed with contrast stretching to suppress intrinsic speckle noise without blurring structural edges. Graph search segmentation using Dijkstra algorithm with a combination of threshold and axial gradient as the cost function is used to segment the retinal layer boundaries. Results: The algorithm was performed and the average thickness of the segmented retina was computed for the 3D retinal scan (128 B-scans) of 8 subjects (4 normal and 4 abnormal) using Early Treatment Diabetic Retinopathy Screening (ETDRS) chart. Conclusion: Segmentation was evaluated using manually segmented B-scan by an Ophthalmologist as ground truth and accuracy was found to be 99.14 ± 0.27%.

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/content/journals/cmir/10.2174/1573405613666171201155119
2019-03-01
2025-07-07
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/content/journals/cmir/10.2174/1573405613666171201155119
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  • Article Type:
    Research Article
Keyword(s): anisotropic diffusion; ETDRS; graph search segmentation; retina; SD-OCT; Speckle
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