Exudates as Landmarks Identified through FCM Clustering in Retinal Images

• Main Authors: Hadi Hamad, Tahreer Dwickat, Domenico Tegolo, Cesare Valenti
• Format: Article
• Language: English
• Published: MDPI AG 2021-12-01
• Series: Applied Sciences
• Subjects: exudates; diabetic retinopathy; segmentation; morphological processing; fuzzy C-means clustering; retinal landmarks
• Online Access: https://www.mdpi.com/2076-3417/11/1/142

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.