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This paper presents a novel automated system that segments six sub-retinallayers from optical coherence tomography (OCT) image stacks of healthy patientsand patients with diabetic macular edema (DME). First, each image in the OCTstack is denoised using a Wiener deconvolution algorithm that estimates theadditive speckle noise variance using a novel Fourier-domain based structuralerror. This denoising method enhances the image SNR by an average of 12dB.Next, the denoised images are subjected to an iterative multi-resolutionhigh-pass filtering algorithm that detects seven sub-retinal surfaces in sixiterative steps. The thicknesses of each sub-retinal layer for all scans from aparticular OCT stack are then compared to the manually marked groundtruth. Theproposed system uses adaptive thresholds for denoising and segmenting eachimage and hence it is robust to disruptions in the retinal micro-structure dueto DME. The proposed denoising and segmentation system has an average error of1.2-5.8 $\mu m$ and 3.5-26$\mu m$ for segmenting sub-retinal surfaces in normaland abnormal images with DME, respectively. For estimating the sub-retinallayer 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 estimatedas 275$\mu m (r=0.92)$ with an average error of 9.3 $\mu m$, while the averagethickness 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 beuseful for tracking the disease progression for DME over a period of time.

Automated OCT Segmentation for Images with DME