Level Set Segmentation of Retinal OCT Images

Optical coherence tomography (OCT) yields high-resolution images of the retina. Reliable identification of the retinal layers is necessary for the extraction of clinically useful information used for tracking the progress of medication and diagnosis of various ocular diseases. Many automatic methods have been proposed to aid with the analysis of retinal layers, mainly, due to the complexity of retinal structures, the cumbersomeness of manual segmentation and variation from one specialist to the other. However, a common drawback suffered by existing methods is the challenge of dealing with image artefacts and inhomogeneity in pathological structures. In this paper, we embed prior knowledge of the retinal architecture derived from the gradient information, into the level set method to segment seven (7) layers of the retina. Mainly, we start by establishing the region of interest (ROI).The gradient edges obtained from the ROI are used to initialise curves for the layers, and the layer topology is used in constraining the evolution process towards the actual layer boundaries based on image forces. Experimental results show our method obtains curves that are close to the manual layers labelled by experts.