# Exudates as Landmarks Identified through FCM Clustering in Retinal Images
###### Abstract  
The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient's status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively. Keywords:exudates; diabetic retinopathy; segmentation; morphological processing; fuzzy C-means clustering; retinal landmarks +
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In the machine-learning literature, many clustering methods have been presented, and each one has advantages and disadvantages. Mainly, these methods are accomplished either with crisp (hard) or fuzzy (soft) clustering. The first defines specific boundaries between clusters; hence, each pixel strictly belongs to a particular cluster. Fuzzy clustering defines data as belonging to more than one cluster with different membership values; as the membership value increases, there is a higher confidence in assigning the object to the correct cluster [7]. It is convenient to use fuzzy clustering in non-deterministic problems, such as overlapping intensities, poor contrast, and noise homogeneities variation. This motivated us to use the fuzzy C-means clustering (FCM) technique to locate the exudates in fundus images, as they can benefit from non-deterministic properties. FCM clustering is becoming more and more popular in many disciplines, for its merits as it is an unsupervised clustering tool that is easy to implement [8], while various applications proved its superiority with respect to other well-known algorithms, such as K-means [9; 10]. Numerous algorithms were developed to detect DR semi-automatically or fully automatically. To the best of our knowledge, they have not been applied to tracking DR progress over time. Typically, the early stages of DR do not show particular symptoms, but they are predominantly present by the time the number and severity of the disease increase [4]. Tracking of DR requires the ability to define exudates as landmarks, to match and to compare their status periodically through exact identification. A supervised classification to detect hard exudates (HE) in human fundus images was proposed in Reference [1]; that method was implemented by applying a contrast adaptive histogram equalization preprocessing to the fundus images and extracting a set of influential features (i.e., standard deviation of the intensity, mean, intensity, size, edge strength, and compactness of the segmented candidates). Those features were used in an echo state neural network, to classify the segments into pathological or normal regions. A set of 50 normal/ pathological fundus images was selected; 35 of the images were used for training and 15 for testing. A system to detect diabetic macular edema through automatic detection of exudates by using the publicly available datasets MESSIDOR, DIARETDB1, and HEI-MED1 (which included 69 patients with different levels of diabetic macular edema) was introduced in Reference [11]. It was designed to automatically detect exudates with some confidence level, in order to separate true/false positives, depending on color-space information and wavelet analysis.