In medical specialty the human eye is a very important diagnostic issue. The problem of retinal detachment is commonly for the people over the age of 50 and it affects the people who had previous eye surgery like cataract removal and also severe eye injuries. The Segmentation in fundus imaging that is a non-trivial task because of the variable size of vessels, comparatively low distinction, and potential presence of pathologies like micro-aneurysm. Many machine learning, deep learning algorithms, have been proposed for this purpose. This paper provides recently invented ideas to improve the technique for blood vessel segmentation to enhance retinal fundus photographic images. Many variants of segmentation methods are considered, including Tyler L. Coye where is an improved version of segmentation methodology used to segment the blood vessels for fundus photography image. The proposed approach was tested and evaluated on Agarwal Eye Hospital’s fundus dataset which consists of 100 photographic images.

Pre-processing, Image Enhancement, Tyler Coye Segmentation Algorithm, Image extraction Retinal Detachment (RD)

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