Exudates as Landmarks Identified through FCM Clustering in Retinal Images

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 a...

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Bibliographic Details

Published in:	Applied Sciences
Main Authors:	Hadi Hamad, Tahreer Dwickat, Domenico Tegolo, Cesare Valenti
Format:	Article
Language:	English
Published:	MDPI AG 2020-12-01
Subjects:	exudates diabetic retinopathy segmentation morphological processing fuzzy C-means clustering retinal landmarks
Online Access:	https://www.mdpi.com/2076-3417/11/1/142