# Exudates as Landmarks Identified through FCM Clustering in Retinal Images
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DIARETDB**} & \multicolumn{3}{c}{**IDRID**} & \multicolumn{3}{c}{**e-Optha**} \\ \cline{2-10}  & \(Se\) & \(Sp\) & \(Acc\) & \(Se\) & \(Sp\) & \(Acc\) & \(Se\) & \(Sp\) & \(Acc\) \\ \hline
**Avg** & 89.0 & 99.3 & 99.2 & 81.5 & 99.5 & 99.2 & 79.3 & 98.9 & 98.8 \\
**Max** & 100.0 & 100.0 & 100.0 & 99.6 & 100.0 & 99.9 & 98.9 & 100.0 & 99.9 \\
**Min** & 38.9 & 94.9 & 94.9 & 67.8 & 97.5 & 97.4 & 42.0 & 94.0 & 94.1 \\
**Std** & 12.4 & 1.0 & 0.9 & 8.0 & 0.4 & 0.4 & 14.2 & 1.1 & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical readings of exudate segmentation for the implemented datasets. Figure 12: Dilating extracted exudates around the OD: without dilation (**a**,**b**) and with dilation, using SE = 45 (**c**,**d**). A review of the results of the training and testing data showed good performance with acceptable variation in extracting the exudates, which can be interpreted as the differences among the datasets; for instance, the e-optha dataset has darker images, while the IDRID dataset has high-quality larger-size dimensions. A numerical comparison of the average performances for the proposed method is shown in Table 3, where each dataset of the used three measures is illustrated explicitly. The first comparison presented here is made to those studies that used FCM to achieve automatic exudates detection, similar to ours (Table 3). Consequently, this provides ophthalmologists with an additional tool with minimal effort to diagnose and treat a part of ophthalmic diseases [33]. The proposed approach is comparable to others for any of the used datasets, especially for _Sp_ and _Acc_. Our methodology outperforms the state-of-the-art approaches in both specificity and accuracy, under the same datasets. As in this work, the performance is measured pixel by pixel, and we notice good results with average \(Se=78.4\%\) for the testing data; on the other hand, the training data came to have \(Se=83.2\%\). This decrease is due to Image #11, which had 60.1% sensitivity. A typical output of the algorithm is represented in Figure 14, noting that the FCM classified those probable exudate pixels into \(C_{1}\), representing non-exudates, and \(C_{2}\), representing exudate pixels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Method** & \(Se\) & \(Sp\) & _Acc_ \\ \hline [PERSON] et al. & FCM & 90.7 & 92.4 & 95.5 \\ [PERSON] & FCM, GA based feature selection & 93.5 & 92.1 & — \\ [PERSON] et al. & Mathematical morphology + FCM & 87.3 & 99.2 & 99.1 \\ [PERSON] & FCM, Feature Set (\(n=100\)) & 100.0 & 87.0 & 90.0 \\ Proposed & Mathematical morphology + FCM & 89.0 & 99.3 & 99.2 \\ (DIARETDB) & Mathematical morphology + FCM & 81.5 & 99.5 & 99.2 \\ Proposed (e-optha) & Mathematical morphology + FCM & 79.3 & 98.9 & 98.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparative study of FCM algorithms to detect exudates. Figure 13: Performance comparison between DIARETDB, IDRID, and e-optha. The proposed algorithm showed remarkably high specificity, which shows its ability to avoid recognizing a non-exudate pixel as an exudate. However, the sensitivity had a lower value due to those exudates with low intensity, which are still hard to detect perfectly [34]. For the sake of completeness, we report for the DIARETDB dataset the best (Image #27 with \(Se=99.2\%\) / \(99.8\%\); Image #16 with \(Acc=97.6\%/99.0\%\)) and the worst cases (Image #16 with \(Se=62.9\%/76.0\%\); Image #27 with \(Acc=93.7\%/94.9\%\)) evaluated, using both pixel by pixel and the extended technique [32]. The same two images present opposite behaviors depending on the measure used (Figure 15). On the other hand, the obtained overall performance of the algorithm on average came to be as follows: \(Se=83.3\%\), \(Sp=99.2\%\), and \(Acc=99.1\%\). The performance of all tested images in DIARETDB is shown in Figure 16. Finally, Table 4 compares state-of-the-art algorithms [15; 16; 35] and our proposed one.