Abstract
The extraction of blood vessels helps in the diagnosis of diseases and to develop advances of medicine. Retinal blood vessel extraction plays a crucial role in early detection and treatment of retinal diseases. This paper provides an automatic segmentation of blood vessels in retinal images. First, the fundus images go through preprocessing steps of image acquisition, grey scale conversion, bias correction and adaptive histogram equalization to enhance the appearance of retinal blood vessels. Then the retinal blood vessels are extracted using a probabilistic modeling and maximum entropy based expectation maximization algorithm which uses maximum entropy uniform distribution as the initial condition. The vessels are more accurately confined using image profiles computed perpendicularly across each of the detected vessel centerline. The algorithm is implemented in MATLAB and the performance is tested on retinal images from DRIVE and STARE databases. When validated, we conclude that the segmentation of retinal images using the proposed method shows a sensitivity of 98.9%, a specificity of 83.74%, and an Accuracy score of 98.8%.
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