  In this paper we give a brief review on the present status of automated
detection systems describe for the screening of diabetic retinopathy. We
further detail an enhanced detection procedure that consists of two steps.
First, a pre-screening algorithm is considered to classify the input digital
fundus images based on the severity of abnormalities. If an image is found to
be seriously abnormal, it will not be analysed further with robust lesion
detector algorithms. As a further improvement, we introduce a novel feature
extraction approach based on clinical observations. The second step of the
proposed method detects regions of interest with possible lesions on the images
that previously passed the pre-screening step. These regions will serve as
input to the specific lesion detectors for detailed analysis. This procedure
can increase the computational performance of a screening system. Experimental
results show that both two steps of the proposed approach are capable to
efficiently exclude a large amount of data from further processing, thus, to
decrease the computational burden of the automatic screening system.
