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Optical coherence tomography (OCT) is a powerful and noninvasive method forretinal imaging. In this paper, we introduce a fast segmentation method basedon a new variant of spectral graph theory named diffusion maps. The research isperformed on spectral domain (SD) OCT images depicting macular and optic nervehead appearance. The presented approach does not require edge-based imageinformation and relies on regional image texture. Consequently, the proposedmethod demonstrates robustness in situations of low image contrast or poorlayer-to-layer image gradients. Diffusion mapping is applied to 2D and 3D OCTdatasets composed of two steps, one for partitioning the data into importantand less important sections, and another one for localization of internallayers.In the first step, the pixels/voxels are grouped in rectangular/cubicsets to form a graph node.The weights of a graph are calculated based ongeometric distances between pixels/voxels and differences of their meanintensity.The first diffusion map clusters the data into three parts, thesecond of which is the area of interest. The other two sections are eliminatedfrom the remaining calculations. In the second step, the remaining area issubjected to another diffusion map assessment and the internal layers arelocalized based on their textural similarities.The proposed method was testedon 23 datasets from two patient groups (glaucoma and normals). The meanunsigned border positioning errors(mean - SD) was 8.52 - 3.13 and 7.56 - 2.95micrometer for the 2D and 3D methods, respectively.

Intra-Retinal Layer Segmentation of 3D Optical Coherence Tomography Using Coarse Grained Diffusion Map