  In this paper, we propose a novel retinal layer boundary model for
segmentation of optical coherence tomography (OCT) images. The retinal layer
boundary model consists of 9 open parametric contours representing the 9
retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variational
functional is first defined to evolve the retinal layer boundary model to
segment the 9 layers simultaneously. By making use of the normals of open
parametric contours, we construct equal sized adjacent narrowbands that are
divided by each contour. Regional information in each narrowband can thus be
integrated into the MS energy functional such that its optimisation is robust
against different initialisations. A statistical prior is also imposed on the
shape of the segmented parametric contours for the functional. As such, by
minimising the MS energy functional the parametric contours can be driven
towards the true boundaries of retinal layers, while the similarity of the
contours with respect to training OCT shapes is preserved. Experimental results
on real OCT images demonstrate that the method is accurate and robust to low
quality OCT images with low contrast and high-level speckle noise, and it
outperforms the recent geodesic distance based method for segmenting 9 layers
of the retina in OCT images.
