  Optical coherence tomography (OCT) is a non-invasive imaging technique that
can produce images of the eye at the microscopic level. OCT image segmentation
to localise retinal layer boundaries is a fundamental procedure for diagnosing
and monitoring the progression of retinal and optical nerve disorders. In this
paper, we introduce a novel and accurate geodesic distance method (GDM) for OCT
segmentation of both healthy and pathological images in either two- or
three-dimensional spaces. The method uses a weighted geodesic distance by an
exponential function, taking into account both horizontal and vertical
intensity variations. The weighted geodesic distance is efficiently calculated
from an Eikonal equation via the fast sweeping method. The segmentation is then
realised by solving an ordinary differential equation with the geodesic
distance. The results of the GDM are compared with manually segmented retinal
layer boundaries/surfaces. Extensive experiments demonstrate that the proposed
GDM is robust to complex retinal structures with large curvatures and
irregularities and it outperforms the parametric active contour algorithm as
well as the graph theoretic based approaches for delineating the retinal layers
in both healthy and pathological images.
