Exudates as Landmarks Identified through FCM Clustering in Retinal Images

Applied Sciences

Cesare Valenti¹  Domenico Tegolo¹  Tahreer Dwickat²  Hadi Hamad² 

[1] Department of Mathematics and Informatics, University of Palermo, 90123 Palermo, Italy;Department of Mathematics, Faculty of Science, An-Najah National University, Nablus P.O. Box 7, Palestine;

关键词: exudates;    diabetic retinopathy;    segmentation;    morphological processing;    fuzzy C-means clustering;    retinal landmarks;   

DOI  :  10.3390/app11010142

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown