BLOOD VESSELS SEGMENTATION FROM RETINAL IMAGES FOR DIABETIC RETINOPATHY DETECTION.

In the modern world, diabetic retinopathy (DR) has become one of the most severe complication prevalent among diabetic patients. The success rate of its curability solemnly depends on the early stage diagnosis or else will lead to total blindness. Blood vessel segmentation of fundus images has obtained considerable importance during the past few years since it facilitates the early detection of eye diseases. Diabetic retinopathy (DR) is effect of diabetes mellitus to the human vision that is the major cause of blindness. Early diagnosis of DR is an important requirement in diabetes treatment. Retinal fundus image is commonly used to observe the diabetic retinopathy symptoms. It can present retinal features such as blood vessel and also capture the pathologies which may lead to DR. Blood vessel is one of retinal features which can show the retina pathologies. It can be extracted from retinal image by image processing with following stages: preprocessing, segmentation, and post-processing. This paper contains a study of public retinal image dataset and several methods for blood vessels segmentation from various conducted researches. There is no further analysis to conclude the best method which can be used for general cases. However, we suggest blood vessels segmentation using Kirsch?s template method that gives the best accuracy in segmentation of blood vessels.