Peer Reviewed

1

DOI

10.17077/omia.1027

Conference Location

Munich, Germany

Publication Date

October 2015

Abstract

In this work, we present a multimodal multiresolution graph-based method to segment the top surface of the retina called the internal limiting membrane (ILM) within optic-nerve-head-centered spectral-domain optical coherence tomography (SD-OCT) volumes. Having a precise 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 multiresolution framework. The retinal blood vessel information (obtained from corresponding registered fundus photographs) along with shape prior information 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.

Rights

Copyright © 2015 Mohammad Saleh Miri, Victor A. Robles, Michael D. Abràmoff, Young H. Kwon, and Mona K. Garvin

Included in

Ophthalmology Commons

Share

COinS
 
Oct 9th, 12:00 AM Oct 9th, 12:00 AM

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

Munich, Germany

In this work, we present a multimodal multiresolution graph-based method to segment the top surface of the retina called the internal limiting membrane (ILM) within optic-nerve-head-centered spectral-domain optical coherence tomography (SD-OCT) volumes. Having a precise 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 multiresolution framework. The retinal blood vessel information (obtained from corresponding registered fundus photographs) along with shape prior information 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.