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In this work, we present a multimodal multiresolution graph- based method to segment the top surface of the retina called the inter- nal limiting membrane (ILM) within optic-nerve-head-centered spectral- domain optical coherence tomography (SD-OCT) volumes. Having a pre- cise ILM surface is crucial as this surface is utilized for measuring several structural parameters such as Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume. The proposed method addresses the common current segmentation errors due to the presence of retinal blood vessels, deep cupping, or a very steep slope of the ILM. In order to resolve these issues, the volume is resampled using a set of gradient vector flow (GVF) based columns. The GVF field is computed according to an initial surface segmentation which is obtained through a multires- olution framework. The retinal blood vessel information (obtained from corresponding registered fundus photographs) along with shape prior in- formation are incorporated in a graph-theoretic approach to compute the ILM segmentation. The method is tested on the SD-OCT volumes from 44 glaucoma subjects and significantly smaller errors were obtained than that from current approaches.

Multimodal Graph-Theoretic Approach for Segmentation of the Internal Limiting Membrane at the Optic Nerve Head