Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2016 (2016), Article ID 7468953, 9 pages
Research Article

Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease

1Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
2Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria

Received 10 May 2016; Revised 29 July 2016; Accepted 2 August 2016

Academic Editor: Chunhui Li

Copyright © 2016 Jing Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In macular spectral domain optical coherence tomography (SD-OCT) volumes, detection of the foveal center is required for accurate and reproducible follow-up studies, structure function correlation, and measurement grid positioning. However, disease can cause severe obscuring or deformation of the fovea, thus presenting a major challenge in automated detection. We propose a fully automated fovea detection algorithm to extract the fovea position in SD-OCT volumes of eyes with exudative maculopathy. The fovea is classified into 3 main appearances to both specify the detection algorithm used and reduce computational complexity. Based on foveal type classification, the fovea position is computed based on retinal nerve fiber layer thickness. Mean absolute distance between system and clinical expert annotated fovea positions from a dataset comprised of 240 SD-OCT volumes was 162.3 µm in cystoid macular edema and 262 µm in nAMD. The presented method has cross-vendor functionality, while demonstrating accurate and reliable performance close to typical expert interobserver agreement. The automatically detected fovea positions may be used as landmarks for intra- and cross-patient registration and to create a joint reference frame for extraction of spatiotemporal features in “big data.” Furthermore, reliable analyses of retinal thickness, as well as retinal structure function correlation, may be facilitated.