Boosting sensitivity of a retinal vessel segmentation algorithm

The correlation between retinal vessel structural changes and the progression of diseases such as diabetes, hypertension, and cardiovascular problems has been the subject of several large-scale clinical studies. However, detecting structural changes in retinal vessels in a sufficiently fast and accurate manner, in the face of interfering pathologies, is a challenging task. This significantly limits the application of these studies to clinical practice. Though monumental work has already been proposed to extract vessels in retinal images, they mostly lack necessary sensitivity to pick low-contrast vessels. This paper presents a couple of contrast-sensitive measures to boost the sensitivity of existing retinal vessel segmentation algorithms. Firstly, a contrast normalization procedure for the vascular structure is adapted to lift low-contrast vessels to make them at par in comparison with their high-contrast counterparts. The second measure is to apply a scale-normalized detector that captures vessels regardless of their sizes. Thirdly, a flood-filled reconstruction strategy is adopted to get binary output. The process needs initialization with properly located seeds, generated here by another contrast-sensitive detector called isophote curvature. The final sensitivity boosting measure is an adoption process of binary fusion of two entirely different binary outputs due to two different illumination correction mechanism employed in the earlier processing stages. This results in improving the noise removal capability while picking low-contrast vessels. The contrast-sensitive steps are validated on a publicly available database, which shows considerable promise in the strategy adopted in this research work.