# Exudates as Landmarks Identified through FCM Clustering in Retinal Images
## 2 Materials and Methods  
Four retinal datasets relevant to exudates were used to train and evaluate the method: DIARETDB0/1, IDRID, and e-optha datasets. The Standard Diabetic Retinopathy Databases Calibration Levels 0/1 (known as DIARETDB0/1) [4] were used to train and also to experimentally validate our automatic exudate detection method. These datasets are publicly available and collect 1152 \(\times\) 1500 true-color loss-less images obtained by using a digital fundus camera with a 50\({}^{\circ}\) field of view. The DIARETDB0 dataset consists of 130 fundus images, of which 20 present normal cases and 110 contain signs of DR (hard exudates, soft exudates, microaneurysms, hemorrhages, and neovascularization). The data correspond to practical situations and can be used to evaluate the general performance of diagnosis methods. This dataset is referred to as \"calibration level 0 fundus images\", and it was published in 2006. On the other hand, the DIARETDB1 dataset consists of 89 fundus images, of which 84 contain at least mild non-proliferative signs (microaneurysms) of DR and 5 are considered to be normal cases which do not contain any sign of DR, according to all experts who participated in the evaluation. The data correspond to a good (not necessarily typical), practical situation. This dataset is referred to as \"calibration level 1 fundus images\", published in 2007. Both of these datasets can be used to evaluate the general performance of diagnostic methods. The ground truth in DIARETDB0 was defined precisely in each image, including both the center coordinates and the type of each pus as an exudate/hemorrhage/cotton-wool area, while, in DIARETDB1, the ground truth was prepared by four experts, showing the affected regions and not specifying the infected areas. A set of random images from these datasets containing hard exudates was segmented manually by our expert ophthalmologist; the set included 19 DIARETDB0 and another 10 DIARETDB1 images affected by DR. Figure 1 shows a sample image from DIARETDB1, together with its ground truth provided in the dataset and created by our ophthalmologist. The ground truths introduced in the two datasets are not suitable for our methodology. The former reports only the center of the exudate, while the latter indicates the affected region in a rough way. A comparison was made between our expert and the DIARETDB1 ground truth (GT), showing 95.7% sensitivity and 90.8% specificity. Another dataset used to evaluate our method was the popular Indian Diabetic Retinopathy Image Dataset (IDRID). The use of this dataset was required for testing the evaluation process, and it was used as an additional testing dataset, without being incorporated into the training process. It was also used to confirm and compare the reliability of our proposed methodology on images outside of the training. The dataset included 81 colored retinal images (jpg files) with resolutions of 4288 \(\times\) 2848 pixels with 50\({}^{\circ}\) fields of view devoted to hard exudates. These images were annotated at the pixel level, in ground truth segmentations that were provided as a binary mask (tif files). The use of 80% of the whole dataset as training and 20% as testing was suggested in Reference [17]. As this work is devoted to the detection of hard exudates, 50 images were selected from this dataset for testing. The fourth dataset used to test and evaluate our proposed algorithm was e-optha [18], which included colored fundus images (jpg files), in addition to the binary masks (png files) that provided exact contoured annotations by an expert ophthalmologist that were revised by another expert. Moreover, healthy patients without lesions were also provided. The dataset contained 47 images with exudates and 35 images without exudates, as well as four different sizes of the images, ranging from 1440 \(\times\) 960 to 2544 \(\times\) 1696 pixels. All images were acquired under the same view angle of 45\({}^{\circ}\). A set of 25 random images was selected.