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An automated method is proposed to segment and quantify the volume of cystoid macular edema (CME) for the abnormal retina with macular hole (MH) in 3D OCT images. The proposed framework consists of three parts: (1) preprocessing, which includes denoising, intraretinal layers segmentation and flattening, MH and vessel silhouettes exclusion; (2) coarse segmentation, in which an AdaBoost classifier is...
This paper proposes a novel image parsing framework to solve the semantic pixel labeling problem from only label strokes. Our framework is based on a network of voters, each of which aggregates both a self voting vector and a neighborhood context. The voters are parameterized using sparse convex coding. To efficiently learn the parameters, we propose a regularized energy function that propagates label...
An automated method is reported for segmenting 3-D fluid-associated abnormalities in the retina, so-called symptomatic exudate-associated derangements (SEAD), from 3-D OCT retinal images of subjects suffering from exudative age-related macular degeneration. In the first stage of a two-stage approach, retinal layers are segmented, candidate SEAD regions identified, and the retinal OCT image is flattened...
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