A Level Set Method with Region-Scalable Fitting Energy for Retinal Layer Segmentation in Spectral-Domain Optical Coherence Tomography Images

Abstract

Retinal layer segmentation of spectral-domain optical coherence tomography images plays an important role during diagnosis and analysis of ophthalmic diseases. In this paper, a novel variational level set framework with region-scalable fitting energy is proposed for automated retinal layer segmentation in SD-OCT. To the best of our knowledge, it is the first time that level set based method succeeds in ten retinal layers segmentation. The proposed framework consists of three steps. First, an anisotropic nonlinear diffusion filter is applied for speckle noise reduction and ROI contrast enhancement. Second, Canny edge detectors are used to extract initial layers: nerve fiber layer, connecting cilia and retinal pigment epithelium. Finally, the rest retinal layers are segmented by means of level set model combined with prior knowledge of retinal thickness and morphology, for which the energy function consists of region-scalable fitting energy data term, area constraint term, regularization term and length penalty term. The proposed method was tested on 50 retinal SD-OCT B-scans from 50 normal subjects. The overall unsigned border position error is 5.92 ± 4.72 μm. The result showed that data terms with border weight terms can keep layer segmentation results in strong border while retaining its fitting capability in weak border. The proposed method achieves better segmentation result than other active contour models.