Scinovex
articleTop 10% cited

A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina

IEEE Transactions on Medical Imaging · 2002 · Vol. 21(10) · pp. 1236–1243
Thomas WalterJ.C. KleinPascale MassinAli Erginay

Abstract

In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. Hence, detection of exudates is an important diagnostic task, in which computer assistance may play a major role. Exudates are found using their high grey level variation, and their contours are determined by means of morphological reconstruction techniques. The detection of the optic disc is indispensable for this approach. We detect the optic disc by means of morphological filtering techniques and the watershed transformation. The algorithm has been tested on a small image data base and compared with the performance of a human grader. As a result, we obtain a mean sensitivity of 92.8% and a mean predictive value of 92.4%. Robustness with respect to changes of the parameters of the algorithm has been evaluated.

Retinal Imaging and AnalysisRetinal Diseases and TreatmentsDigital Imaging for Blood DiseasesArtificial intelligenceDiabetic retinopathyRobustness (evolution)Optic discComputer visionComputer scienceSensitivity (control systems)Fundus (uterus)RetinaOphthalmology

MeSH terms

AlgorithmsColorDiabetic RetinopathyExudates and TransudatesFluorescein AngiographyFundus OculiHumansImage EnhancementImage Interpretation, Computer-AssistedOphthalmoscopyOptic DiskPattern Recognition, AutomatedQuality ControlRetinaSensitivity and Specificity
Citations
820
FWCI
3.31
field-weighted impact
References
26
Percentile
92%
vs. same field & year
Citations per year
References
Citation Network

How this paper connects to the literature. Drag to explore, click any node to open that paper.