A Two-phase Decision Support Framework for the Automatic Screening of Digital Fundus Images

TL;DR

This paper proposes a two-phase decision support framework for diabetic retinopathy screening in digital fundus images, combining pre-screening and lesion detection to improve efficiency and reduce computational load.

Contribution

It introduces a novel two-step detection procedure with a new feature extraction method based on clinical observations, enhancing screening performance.

Findings

Efficient exclusion of non-pertinent data reduces processing time.

Pre-screening effectively filters out severely abnormal images.

Region-based lesion detection improves diagnostic accuracy.

Abstract

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.