# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 3 Results  
### Retinal Layer Segmentation and Thickness Computation  
The graph-based automated segmentation algorithm successfully delineated seven boundaries within the SD-OCT images, corresponding to six distinct retinal layers. These layers, segmented from both foveal and non-foveal slices, include the internal limiting membrane (ILM) at the topmost boundary, followed by the retinal nerve fiber layer and ganglion cell layer (RNFL + GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer and inner segment (ONL + IS), and the outer segment and retinal pigmented epithelium (OS + RPE). Figure 5 illustrates these segmented layers, with each layer's abbreviation and full name provided in the legend for clarity. Table 1 quantifies the mean thicknesses (\(\pm\)standard deviation) of each of the retinal layers (in microns) segmented by the algorithm for the foveal and non-foveal slices, respectively, across the entire dataset.  
Gaussian Filter-Based Denoising Versus Wavelet-Based Denoising and Their Impacts on the Segmentation Results  
Figure 6a,b depicts a sample SD-OCT image from the test dataset after being denoised using a simple Gaussian filter and the corresponding segmentation results--zoomed into the user-defined ROI--produced by the graph-based algorithm, respectively. The algorithm was able to segment the retinal layers effectively, providing clear delineation of each boundary.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Layer**} & **Segmented Intra-Retinal Layers (as Shown in Figure 1)** & **Thickness = Mean \(\pm\) SD (in Microns)** \\ \hline
1 & RNFL + GCL & 25.02 \(\pm\) 3.16 \\ \hline
2 & IPL & 5.40 \(\pm\) 2.79 \\ \hline
3 & INL & 5.94 \(\pm\) 1.10 \\ \hline
4 & OPL & 8.45 \(\pm\) 0.96 \\ \hline
5 & ONL + IS & 16.24 \(\pm\) 1.76 \\ \hline
6 & OS + RPE & 12.67 \(\pm\) 6.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean thicknesses of the six layers segmented by the algorithm in the foveal slices of the SD-OCT images across the test dataset.  
Figure 5: An illustration of the foveal slice of a sample macular SD-OCT image from the test dataset illustrating the layers and boundaries segmented by the algorithm.  
The results of denoising using a bilateral filter followed by wavelet reconstruction for a sample SD-OCT image are shown in Figure 7a, Figure 7b and Figure 7c, respectively. The corresponding segmentation results, illustrated in Figure 7c, demonstrate the effectiveness of this advanced denoising technique in preserving edge details and enhancing boundary detection.  
The graph-based algorithm computed the mean thicknesses of the six segmented layers for each SD-OCT image within the test dataset. Table 2 quantifies the retinal layer thicknesses (in microns) of the segmented image and the mean computation time per image over the entire dataset, when executed on a specific computer (64-bit Windows10 OS with Intel Core i5 processor, 8 GB RAM, and 1 GB Radeon graphics card), after being denoised by Gaussian filtering and wavelet-based denoising techniques.  
Figure 6: An illustration of the (**a**) macular SD-OCT scan of healthy retina (**b**) the image segmented by graph-based algorithm after denoising the image with a Gaussian filter showing 7 delineated boundaries.  
Figure 7: An illustration of the (**a**) macular SD-OCT image of healthy retina after bilateral filtering (**b**) the image after wavelet-based detail thresholding (**c**) the segmented image showing 7 delineated boundaries.