Title
A proposal of extracting retinal boundaries for optical coherence tomography using three dimensional active grid
Abstract
Three dimensional process could improve the extraction rate in comparison with two dimensional process. We propose a new three dimensional active grid for extraction of retinal boundaries on optical coherence tomography. Ophthalmologist desire a system that can measure retinal thickness automatically for quantitative evaluation on retinal diseases. Conventional method tried extract boundaries of retinal layer i.e. inner limiting membrane (ILM) and retinal pigment epithelium (RPE). The boundaries in normal retina are extracted almost completely. However, extracted boundaries in disease retina included some errors. Therefore we propose a new method for extracting the boundaries of retinal layer from three-dimensional optical coherence tomography (OCT) images. We use 3D active grid which is dynamic shape model. This paper applied the proposed method to 22 OCT image sets (10 sets of normal retinal images and 12 sets of disease retinal images). Retinal thickness in normal retinal images are obtained by accuracy of 97.54 %, and those in disease retinal images are done by accuracy of 72.32 % compared with the judgment of medical doctor. The proposed method is indicated as an efficient method for extraction of retinal border lines.
Year
DOI
Venue
2012
10.1109/SCIS-ISIS.2012.6505366
SCIS&ISIS
Keywords
Field
DocType
diseases,eye,medical image processing,optical tomography,3d active grid,ilm,oct images,rpe,disease retinal image,dynamic shape model,extraction rate,inner limiting membrane,optical coherence tomography,retinal boundary,retinal pigment epithelium,retinal thickness,three dimensional active grid,active grid,macular,retina
Computer vision,Optical coherence tomography,Disease retinal,Computer science,Retina,Normal retina,Artificial intelligence,Retinal,Retinal pigment epithelium,Grid,Inner limiting membrane
Conference
ISSN
ISBN
Citations 
2377-6870
978-1-4673-2742-8
0
PageRank 
References 
Authors
0.34
0
7