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Journal of Electrical and Computer Engineering
Volume 2017 (2017), Article ID 4362603, 12 pages
https://doi.org/10.1155/2017/4362603
Research Article

3D Curvelet-Based Segmentation and Quantification of Drusen in Optical Coherence Tomography Images

1Department of Bioelectrics and Biomedical Engineering, Medical Image & Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2Department of Advanced Technologies in Medicine, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

Correspondence should be addressed to H. Rabbani; moc.oohay@bar_ssoh

Received 26 July 2016; Revised 9 December 2016; Accepted 26 December 2016; Published 31 January 2017

Academic Editor: Panajotis Agathoklis

Copyright © 2017 M. Esmaeili 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.

Abstract

Spectral-Domain Optical Coherence Tomography (SD-OCT) is a widely used interferometric diagnostic technique in ophthalmology that provides novel in vivo information of depth-resolved inner and outer retinal structures. This imaging modality can assist clinicians in monitoring the progression of Age-related Macular Degeneration (AMD) by providing high-resolution visualization of drusen. Quantitative tools for assessing drusen volume that are indicative of AMD progression may lead to appropriate metrics for selecting treatment protocols. To address this need, a fully automated algorithm was developed to segment drusen area and volume from SD-OCT images. The proposed algorithm consists of three parts: (1) preprocessing, which includes creating binary mask and removing possible highly reflective posterior hyaloid that is used in accurate detection of inner segment/outer segment (IS/OS) junction layer and Bruch’s membrane (BM) retinal layers; (2) coarse segmentation, in which 3D curvelet transform and graph theory are employed to get the possible candidate drusenoid regions; (3) fine segmentation, in which morphological operators are used to remove falsely extracted elongated structures and get the refined segmentation results. The proposed method was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system. The average true positive and false positive volume fractions (TPVF and FPVF) for the segmentation of drusenoid regions were found to be 89.15% ± 3.76 and 0.17% ± .18%, respectively.