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