Paper Info
Title
A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes.
Abstract
Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer (RNFL). Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new approach for locating the Bruch's membrane opening BMO and then estimating the optic disc size and rim area of 3D Spectralis SD-OCT images. To deal with the overlapping of the Bruch's membrane BM layer and the border tissue of Elschnig due to the poor image resolution, we propose the use of image deconvolution approach to separate these layers. To estimate the optic disc size and rim area, we propose the use of a new regression method based on the artificial neural network principal component analysis (ANN-PCA), which allows us to model irregularity in the BMO estimation due to scan shifts and/or poor image quality. The diagnostic accuracy of rim area, and rim to disc area ratio is compared to the diagnostic accuracy of global RNFL thickness measurements provided by two commercially available SD-OCT devices using receiver operating characteristic curve analyses.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6944468
EMBC
Keywords
Field
DocType
glaucoma monitoring,eye,optical tomography,receiver operating characteristic curve analyses,neuroretinal rim area,quantitative tools,image quality,rim to disc area ratio,optical imaging technique,diseases,ann-pca,neurophysiology,retinal ganglion cell loss,optic disc size,regression method,image deconvolution approach,artificial neural network principal component analysis,image resolution,regression analysis,patient monitoring,model irregularity,healthy eyes,onh topography,glaucoma diagnosing,sd-oct devices,deconvolution,glaucoma,scan shifts,glaucoma eyes,structural changes,diagnostic accuracy,bmo estimation,elschnig,retinal nerve fiber layer,bruch's membrane bm layer,hierarchical framework,vision defects,chronic neurodegenerative disease,3d spectral domain optical coherence tomography optic nerve head images,3d spectralis sd-oct images,biological tissues,biomembranes,sd-oct,noninvasive imaging,principal component analysis,border tissue,rim area,global rnfl thickness measurement,neural nets,medical image processing,sensitivity analysis,coherence,tomography,image segmentation,optical imaging,accuracy
Computer vision,Retinal ganglion,Optical coherence tomography,Glaucoma,Nerve fiber layer,Computer science,Image quality,Tomography,Artificial intelligence,Image resolution,Optic nerve
Conference
Volume
ISSN
Citations 
2014
1557-170X
4
PageRank 
References 
Authors
0.54
3
4
Name
Order
Citations
PageRank
Akram Belghith1224.99
Christopher Bowd2224.53
Robert N Weinreb3589.90
Linda M Zangwill4143.19