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In this paper, we propose a novel retinal layer boundary model forsegmentation of optical coherence tomography (OCT) images. The retinal layerboundary model consists of 9 open parametric contours representing the 9retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variationalfunctional is first defined to evolve the retinal layer boundary model tosegment the 9 layers simultaneously. By making use of the normals of openparametric contours, we construct equal sized adjacent narrowbands that aredivided by each contour. Regional information in each narrowband can thus beintegrated into the MS energy functional such that its optimisation is robustagainst different initialisations. A statistical prior is also imposed on theshape of the segmented parametric contours for the functional. As such, byminimising the MS energy functional the parametric contours can be driventowards the true boundaries of retinal layers, while the similarity of thecontours with respect to training OCT shapes is preserved. Experimental resultson real OCT images demonstrate that the method is accurate and robust to lowquality OCT images with low contrast and high-level speckle noise, and itoutperforms the recent geodesic distance based method for segmenting 9 layersof the retina in OCT images.

OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the Mumford-Shah functional