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.