Paper Info
Title
Automated Segmentation Of Intraretinal Cystoid Macular Edema For Retinal 3d Oct Images With Macular Hole
Abstract
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 used to get the seeds and constrained regions for Graph Cut; (3) fine segmentation, in which a graph cut algorithm is used to get the refine segmentation result. The proposed method was evaluated in 3D OCT images from 18 typical patients with CMEs and MH. The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and accuracy rate (ACC) for CME volume segmentation are 84.6%, 1.7% and 99.7%, respectively.
Year
Venue
Keywords
2015
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
cystoid macular edema (CME), Macular hole (MH), segmentation, AdaBoost, Graph Cut, optical coherence tomography (OCT)
Field
DocType
ISSN
Cut,Computer vision,Pattern recognition,Macular edema,Computer science,Segmentation,Feature extraction,Image segmentation,Artificial intelligence,Retinal,Macular hole,Adaboost classifier
Conference
1945-7928
Citations 
PageRank 
References 
6
0.64
6
Authors
5
Name
Order
Citations
PageRank
Li Zhang1292.37
Weifang Zhu28515.92
Fei Shi38612.91
Hao-Yu Chen49715.08
XinJian Chen550253.39