Optical coherence tomography (OCT) is known to be one of the powerful and noninvasive methods in retinal imaging. OCT uses retroreflected light to provide micron-resolution, cross-sectional scans of biological tissues. In contrast to OCT technology development, which has been a field of active research since 1991, OCT image segmentation has only been fully explored during the last decade. In this paper, we introduce a fast segmentation method based on a new kind of spectral graph theory named diffusion maps. The research is performed on spectral domain OCT images depicting normal macular appearance. In contrast to our recent methods of graph based OCT image segmentation, the presented approach does not require edge-based image information and rather relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. This method is tested on thirteen 3D macular SD-OCT images obtained from eyes without pathologies with Topcon 3D OCT-1000 imaging system (with a size of 650 × 512 × 128 voxels and a voxel resolution of 4.81 × 13.67 × 24.41 μm3). The mean unsigned and signed border positioning errors (mean ± SD) was 8.52±3.13 and -4.61±3.35 micrometers, respectively. The average computation time of the proposed algorithms (implemented with MATLAB) was 12 seconds per 2D slice.
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Intra-retinal layer segmentation of optical coherence tomography using diffusion map
Semantic Scholar · Engineering · 2013
Abstract
Optical coherence tomography (OCT) is known to be one of the powerful and noninvasive methods in retinal imaging. OCT uses retroreflected light to provide micron-resolution, cross-sectional scans of biological tissues. In contrast to OCT technology development, which has been a field of active research since 1991, OCT image segmentation has only been fully explored during the last decade. In this paper, we introduce a fast segmentation method based on a new kind of spectral graph theory named diffusion maps. The research is performed on spectral domain OCT images depicting normal macular appearance. In contrast to our recent methods of graph based OCT image segmentation, the presented approach does not require edge-based image information and rather relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. This method is tested on thirteen 3D macular SD-OCT images obtained from eyes without pathologies with Topcon 3D OCT-1000 imaging system (with a size of 650 × 512 × 128 voxels and a voxel resolution of 4.81 × 13.67 × 24.41 μm3). The mean unsigned and signed border positioning errors (mean ± SD) was 8.52±3.13 and -4.61±3.35 micrometers, respectively. The average computation time of the proposed algorithms (implemented with MATLAB) was 12 seconds per 2D slice.