# Exudates as Landmarks Identified through FCM Clustering in Retinal Images
## 4 Experimental Results and Discussion  
Testing our method on the four publicly available datasets, which are relevant to hard exudates, makes the method more reliable. The overall number of images used to train and evaluate the performance was 114 images for both testing and training. The randomly selected images were of good quality, no patient was duplicated, and disease stratification was represented. The collection of images for testing was distributed as follows: 25 from DIARETTDB0/1, 50 from IDRID, and finally 25 from the e-optha dataset. Notice that, in supervised classifiers, a huge set of GT is needed to define and calibrate the methodology, while even with a reasonably small number of chosen images, the FCM was well calibrated and efficiently classified exudates. In order to verify the correctness of the proposed methodology, we compared the output against the valuation provided by our expert ophthalmologist for the DIARETTDB0/1 dataset, while for the other datasets, IDRID and e-optha, the provided ground truths were used. The habitually used performance measurements were used to report performance: sensitivity, specificity, positive predictive value, and accuracy. Indeed, labeling each pixel with respect to the ground truth leads to the following cases: true positive (TP), when a pixel is properly considered an exudate; false positive (FP), when a pixel is wrongly considered an exudate; false negative (FN), when a pixel is wrongly considered a non-exudate; and true negative (TN), when a pixel is properly considered a non-exudate. A high value of TP indicates correct identification of exudates [21; 26; 27; 28]. The method of evaluation is in need of further performance measures, which can be derived from the state pixels to define the overall performance of the methodology. In this paper, two strategies were implemented to evaluate the abilities of our approach. The classical method, to define the four classes (TP, TN, FP, and FN), was achieved by summing up the corresponding pixels between the GT and the resulting segmented image according to their mutual relation. Another method of evaluating the performance of exudate segmentation in retinal fundus images was, however, driven by the expert. The ophthalmologists did not totally agree with each other in their manual GT segmentation regarding exudates for many reasons: the state of the image (clear, blur, sharpen, brightness, outliers, etc. ), a complication of the case, the spherical shape of the fundus, etc. This inconsistency in the GT segmentation is evident in other retinal issues, such as vessels: segmentation, crossings, and bifurcations [29], in addition to the optic disc and other landmarks of the retina. This makes the segmentation task quite difficult because no definite and general-purpose rules are followed. Indeed, the authors of References [15; 16; 30; 31] believe that it is more reasonable to consider those connected segmented areas as TP, even though they partially intersect with the GT under particular constraints [32]. In other words, those segmented exudates are considered to be TP, even if the contours are not matched exactly, if and only if the connected components of the segmented portions touch the GT and satisfy a defined ratio of overlapping. An example is given in the following: Let us suppose a GT and a corresponding segmentation are given as presented in Figure 9a,b, and then each overlapping region between a part of the GT and a segmented portion should exceed a factor \(\sigma=0.2\); therefore, the neighboring pixels connected to this part of the relevant GT and segment will be considered as TP pixels [32].