An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

Adal, K.M. (TU Delft ImPhys/Quantitative Imaging)
Van Etten, Peter G. (Rotterdam Eye Hospital)
Martinez, Jose P (Rotterdam Eye Hospital)
Rouwen, Kenneth W. (Rotterdam Eye Hospital)
Vermeer, K.A. (TU Delft ImPhys/Quantitative Imaging; Rotterdam Eye Hospital)
van Vliet, L.J. (TU Delft ImPhys/Quantitative Imaging)

2017

People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy. Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and inter-visit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time-points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of 80% at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.

Computer-aided detection
Diabetes
diabetic retinopathy screening
fundus images
Image color analysis
Lesions
Lighting
longitudinal analysis
red lesions
Retina
Retinopathy
Robustness

http://resolver.tudelft.nl/uuid:cabd305b-3a41-4b22-aaff-9fdd1279daa2

https://doi.org/10.1109/TBME.2017.2752701

0018-9294

IEEE Transactions on Biomedical Engineering, PP (99), 1382-1390

Institutional Repository

journal article

© 2017 K.M. Adal, Peter G. Van Etten, Jose P Martinez, Kenneth W. Rouwen, K.A. Vermeer, L.J. van Vliet