Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes

Retinal vessel segmentation plays a key role in the detection of numerous eye diseases, and its reliable computerised implementation becomes important for automatic retinal disease screening systems. A large number of retinal vessel segmentation algorithms have been reported, primarily based on three main steps including making the background uniform, second-order Gaussian detector application and finally the region-grown binarization. Although these methods improve the accuracy levels, their sensitivity to low-contrast vessels still needs attention. In this paper, some contrast-sensitive approaches are discussed that once embedded in the conventional algorithm results in improved sensitivity for a given retinal vessel extraction technique. The impact of these add-on modules is assessed on publicly available databases like DRIVE and STARE and found to provide promising results.