  With the introduction of spectral-domain optical coherence tomography
(SDOCT), much larger image datasets are routinely acquired compared to what was
possible using the previous generation of time-domain OCT. Thus, there is a
critical need for the development of 3D segmentation methods for processing
these data. We present here a novel 3D automatic segmentation method for
retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume
datasets are obtained by using a 3D smoothing filter and a 3D differential
filter. Their linear combination is then calculated to generate new volume data
with an enhanced boundary surface, where pixel intensity, boundary position
information, and intensity changes on both sides of the boundary surface are
used simultaneously. Next, preliminary discrete boundary points are detected
from the A-Scans of the volume data. Finally, surface smoothness constraints
and a dynamic threshold are applied to obtain a smoothed boundary surface by
correcting a small number of error points. Our method can extract retinal layer
boundary surfaces sequentially with a decreasing search region of volume data.
We performed automatic segmentation on eight human OCT volume datasets acquired
from a commercial Spectralis OCT system, where each volume of data consisted of
97 OCT images with a resolution of 496 512; experimental results show that this
method can accurately segment seven layer boundary surfaces in normal as well
as some abnormal eyes.
