| 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. |