Retinal images are influenced by all the factors that affect the body vasculature in general. The human eye is a unique region of the human body where the vascular condition can be directly observed. In addition to fovea and optic disc, the blood vessels contributes one of the main features of a retinal fundus image and several of its properties are noticeably affected by worldwide major diseases such as diabetes, hypertension, and arteriosclerosis. Blood vessel segmentation of retinal images plays an important role in the diagnosis of eye diseases. Automatic and accurate blood vessel segmentation system could provide several useful features for diagnosis of various retinal diseases, and reduce the doctors’ workload. However, the retinal images have low contrast, and large variability is presented in the image acquisition process, which deteriorates automatic blood vessel segmentation results. For improving the segmentation results, we construct a multi-dimensional feature vector with the green channel intensity and the enhanced intensity feature by the morphological operation. Blood vessel segmentation of retinal images plays an important role in the diagnosis of eye diseases. In this project, the system propose an automatic unsupervised blood vessel segmentation method for retinal images. Firstly, a multidimensional feature vector is constructed with the green channel intensity and the vessel enhanced intensity feature by the morphological operation. However, the retinal images have low contrast, and large variability is presented in the image acquisition process, which deteriorates automatic blood vessel segmentation results.