Automated identification of diabetic retinal exudates in digital colour images

Abstract

Aim: To identify retinal exudates automatically from colour retinal images.

Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated.

Results: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification.

Conclusions: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.

Figures

Figure 1

Colour normalisation and local contrast enhancement: (A) reference image, (B) typical retinal image (including exudates), (C) colour normalised version, (D) after contrast enhancement.

Figure 2

Colour image segmentation: (A) FCM segmented image, (B) candidate exudate regions overlaid on the original image, and (C) final classification (after subsequent neural network classification).

Figure 3

Performance of the BP neural network as a function of output threshold.