## 4 Discussion and Conclusions  
In this paper, we have investigated and proposed methods to automatically extract exudates from images taken from diabetic patients with non-dilated pupils. The work is based on the FCM clustering segmentation and morphological techniques. Four input features based on the characteristics of exudates, namely intensity, standard deviation, hue and number of edge pixels, are selected. Blood vessels and optic disc pixels are also removed from the fourth feature in order to prevent misclassification. The performance of the algorithm is measured against ophthalmologists' hand-drawn ground-truth. Sensitivity, specificity, PPV and PLR are used as the performance measurement of exud detection because they combine true positive and false positive rates. Accuracy values are also used to evaluate the system. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**24-bit images** & **TP** & **FP** & **FN** & **TN** & **Sensitivity** & **Specificity** & **PPV** & **PLR** & **Accuracy** \\  & & & & & **(\%)** & **(\%)** & **(\%)** & & **(\%)** \\ \hline Image1 & 4898 & 1677 & 741 & 368684 & 86.86 & 99.55 & 74.49 & 191.83 & 99.36 \\ Image2 & 1019 & 350 & 61 & 374570 & 94.35 & 99.91 & 74.43 & 1010.70 & 99.89 \\ Image3 & 81 & 3548 & 27 & 372344 & 75.00 & 99.06 & 2.23 & 79.46 & 99.05 \\ Image4 & 838 & 2294 & 227 & 372641 & 78.69 & 99.39 & 26.76 & 128.60 & 99.33 \\ Image5 & 1247 & 4978 & 67 & 369708 & 94.90 & 98.67 & 20.03 & 71.43 & 98.66 \\ Image6 & 1479 & 2905 & 204 & 371412 & 87.88 & 99.22 & 33.74 & 113.23 & 99.17 \\ Image7 & 126 & 2445 & 23 & 373406 & 84.56 & 99.35 & 4.90 & 129.99 & 99.34 \\ Image8 & 1234 & 2734 & 525 & 371507 & 70.15 & 99.27 & 31.10 & 96.03 & 99.13 \\ Image9 & 381 & 322 & 38 & 375259 & 90.93 & 99.91 & 54.20 & 1060.62 & 99.90 \\ Image10 & 424 & 1729 & 76 & 373771 & 84.80 & 99.54 & 19.69 & 184.17 & 99.52 \\ \hline \end{tabular}
\end{table} 
Table 1: The example results of exudates detection from FCM clustering followed by morphological operator \begin{table}
\begin{tabular}{l|c|c|c|c|c|c} \hline
**Method** & **Sensitivity (\%)** & **Specificity (\%)** & **PPV (\%)** & **PLR** & **Accuracy (\%)** \\ \hline FCM clustering & 97.2 & 85.4 & 5.9 & 7.9 & 85.6 \\ FCM clustering and morphological & 87.2 & 99.2 & 42.7 & 224.2 & 99.1 \\ Nearest neighbor & 90.4 & 96.6 & 28.6 & 6.2 & 96.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of average result from FCM clustering only, FCM clustering followed by morphological reconstruction and nearest neighbor The result shows that PPV, PLR and accuracy values increase when the FCM clustering technique is combined with morphological technique. If any applications need to detect maximum number of exudate pixels or require more execution speed, the FCM clustering technique could be used in isolation. However, if the applications require higher accuracy, the FCM clustering combined with the morphological technique should be chosen. There are some incorrect exudate detections which are caused by the artifacts that are similar to exudates, artifacts from noise in the image acquisition process, the exudates that are proximate to blood vessels or exudates that appear very faint. These missing faint exudates may have not affected the sensitivity much since even human experts are not sure about some ambiguous regions. However, the performance of the algorithm can be improved if these set of low-contrast exudates can be detected. This system intends to help ophthalmologists in diabetic retinopathy screening process to detect symptoms faster and more easily. This is not a final-result application but it can be a preliminary diagnosis tool or decision support system for ophthalmologists. Human ophthalmologists are still needed for the cases where detection results are not very obvious. One main weakness of the algorithm arises from the fact that the algorithm depends on other tasks, namely, the detection of optic disc, and vessel removal. The result of exudate detection depends on the success of these methods.