Edge Detection for Retinal Image Using Superimposing Concept and Curvelet Transform

Retinal image analysis plays vital role in many applications, such as finding out ocular fund us and early stage detection of some disease. Even though there is more advanced technologies for human recognition process this retinal image analysis concentrates on the human iris authentication purpose. Since both the similar person can’t have the same retinal blood vessels. As per this paper concern in extracting of retinal blood vessels from the retina images as well as finding out disease affected area in the retina image to do this process the methodologies implemented are multi structure elements makes the edge detection effectively. Hence morphology operators using multi structure elements method are used to find out the ridges. Afterwards morphological operators by reconstruction eliminate the ridges which are not belonging to the blood vessels. The algorithm used here is back propagation (BPA) which helps to find out the real retinal blood vessels from the image. The researchers also have more reputed works on this area to produce better result for blood vessel detection.