# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 3 Results
### Performance Evaluation
#### 3.3.1 Segmentation Accuracy with Respect to Previous Studies  
The automated segmentation algorithm effectively segmented the retinal layers, with the ILM being the topmost boundary, followed by RNFL + GCL, IPL, INL, OPL, ONL + IS, and OS + RPE. The mean layer thicknesses, in microns, computed by the algorithm for the foveal SD-OCT scans, after Gaussian filtering, were compared to the normalized layer thickness values reported by a previously published study. This was conducted to validate the findings from our method in terms of the retinal layer thicknesses determined by our algorithm when compared to those computed by other already published studies. Although the data being compared might have been collected from different demographics and devices, the values were averaged across the datasets to omit the finer details of each individual and normalized to reflect similarity between the retinal layers being compared. For normalization, multiple consecutive layers were combined based on the layers segmented by the current graph-based algorithm. The mean of the thickness values reported by the previous study across nine macular sectors were determined for each of the combined layers, to compare with corresponding mean layer thickness values from the current study, by one-sample \(t\)-tests for individual layers. Table 3 summarizes the statistical results from this comparative analysis, showing that the differences between the mean (\(\pm\)SD) thicknesses of each retinal layer and those reported by the previous study were statistically insignificant (one-sample \(t\)-test, \(p\) > 0.05), except for layer 4 (\(p\) = 0.04). The overall mean retinal thickness computed by the algorithm did not vary significantly (\(p\) = 0.17) compared to the reference values, confirming the accuracy of the segmentation of the newly developed algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{3}{*}{**Layer**} & \multirow{3}{*}{\
\begin{tabular}{c} **Segmented** \\ **Intra-Retinal** \\ **Layers (as** \\ **Shown in** \\ **Figure 1)** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **Gaussian Filter-Based** \\ **Denoising** \\ \end{tabular} } & \multirow{3}{*}{**Wavelet-Based**} & \multirow{3}{*}{**Denoising**} \\ \cline{3-3} \cline{5-6}  & & & & **Mean** \(\pm\) SD** \\ \cline{3-3} \cline{5-6}  & & **Tickness =** \\ \cline{3-3} \cline{5-6}  & & **Mean \(\pm\) SD** \\ \cline{3-3} \cline{5-6}  & & **Time (in Seconds)** & **(in Microns)** \\ \hline
1 & RNFL + GCL & 25.16 \(\pm\) 2.09 & & 25.29 \(\pm\) 2.21 & \\ \hline
2 & IPL & 5.38 \(\pm\) 1.23 & & 5.49 \(\pm\) 1.18 & \\ \hline
3 & INL & 5.29 \(\pm\) 0.15 & & 6.15 \(\pm\) 1.89 & \\ \hline
4 & OPL & 9.01 \(\pm\) 2.85 & & 8.19 \(\pm\) 2.51 & \\ \hline
5 & ONL + IS & 16.95 \(\pm\) 2.24 & & 14.88 \(\pm\) 2.62 & \\ \hline
6 & OS + RPE & 14.92 \(\pm\) 2.59 & & 14.96 \(\pm\) 2.62 & \\ \hline \end{tabular}
\end{table} 
Table 2: Mean thicknesses of the six layers between the seven boundaries segmented by the algorithm in the macular SD-OCT images over the entire dataset, when the images were denoised using Gaussian filter and wavelet reconstruction, respectively, prior to segmentation. To evaluate the impact of the two different denoising techniques implemented in the study, another comparative analysis was performed between the thicknesses computed by our algorithm when denoised by either Gaussian filtering or the wavelet-based denoising technique to that of the results from the previous study. Figure 8 summarizes the results from the comparative analysis of the mean layer thicknesses computed by this novel segmentation algorithm (after simple Gaussian filter-based denoising and after advanced denoising using wavelet-based thresholding) and the normalized layer thickness values from a previously reported study. The comparison of the reference algorithm with the current segmentation algorithm when images were denoised using simple Gaussian filter, shows no statistically significant  
Figure 8: Histograms illustrating the mean thickness values from [PERSON] et al.