A simple review on supervised blood vessel segmentation methodologies in retinal images along with databases

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. It helps in not only detecting the retinal diseases but also can help to recover that disease in time like diabetic retinopathy, retinopathy of prematurity (ROP) etc. Supervised Retinal vessel segmentation algorithms are most widely research and studied by researcher. As more research work is published on supervised algorithm we are decided to work on this paper. In this work we are simply examines the supervised blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera along with a survey along with provide the most of the databases which are locally present for this work. The aim of this paper is to review and analyze the supervised retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures given by the different authors in systematic form. We trying to provide the reader a framework for the existing research; to introduce the all supervised retinal vessel segmentation algorithms along with databases which are locally present over for work and future directions and summarize the survey. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.