A Robust Segmentation of Blood Vessels on Fundus Images Using Vessel Extraction and Classification

Diabetic retinopathy is a disease, which forms a severe threat on sight. It may reach to blindness among working age people. By analyzing and detecting vasculature structures in retinal image the diabetes can be detected in advanced stages by comparing its states of retinal blood vessels. In blood vessel segmentation approach, computer based retinal image analysis can be used to extract the retinal image vessels. Mathematical morphology and K-means clustering are used to segment the vessels. To enhance the blood vessels and suppress the background information, smoothing operation can be performed on the retinal image using mathematical morphology. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. The optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected by using mathematical morphology methods such as closing, filling, dilation and erosion. Then the enhanced image is segmented using K-means clustering algorithm to detect the diseases easily. Keywords-Vasculature, Vessel Segmentation, Mathematical Morphology, Clustering, Dilation, Erosion.