  This paper presents a novel automated system that segments six sub-retinal
layers from optical coherence tomography (OCT) image stacks of healthy patients
and patients with diabetic macular edema (DME). First, each image in the OCT
stack is denoised using a Wiener deconvolution algorithm that estimates the
additive speckle noise variance using a novel Fourier-domain based structural
error. This denoising method enhances the image SNR by an average of 12dB.
Next, the denoised images are subjected to an iterative multi-resolution
high-pass filtering algorithm that detects seven sub-retinal surfaces in six
iterative steps. The thicknesses of each sub-retinal layer for all scans from a
particular OCT stack are then compared to the manually marked groundtruth. The
proposed system uses adaptive thresholds for denoising and segmenting each
image and hence it is robust to disruptions in the retinal micro-structure due
to DME. The proposed denoising and segmentation system has an average error of
1.2-5.8 $\mu m$ and 3.5-26$\mu m$ for segmenting sub-retinal surfaces in normal
and abnormal images with DME, respectively. For estimating the sub-retinal
layer thicknesses, the proposed system has an average error of 0.2-2.5 $\mu m$
and 1.8-18 $\mu m$ in normal and abnormal images, respectively. Additionally,
the average inner sub-retinal layer thickness in abnormal images is estimated
as 275$\mu m (r=0.92)$ with an average error of 9.3 $\mu m$, while the average
thickness of the outer layers in abnormal images is estimated as 57.4$\mu m
(r=0.74)$ with an average error of 3.5 $\mu m$. The proposed system can be
useful for tracking the disease progression for DME over a period of time.
