An Efficient Retinal Blood Vessel Segmentation Using Morphological Operations

Abstract

The structure of retinal vessel carries information about many diseases. It is difficult to analyze this complex structure by human eye. Additionally, it has time-consuming process. In this study, an extremely lower complex and more successful retinal blood vessel segmentation method is proposed via using morphological operators. Colorful retinal images are divided into red, green and blue channels. Green channel is preferred for segmentation on the account of including clear details about retinal vessels. Then, adaptive threshold with 5x5 Gaussian window is applied in order to obtain clean vessel geometry. In the next step, retinal image is sharpened and then, 3x3 wiener filter is applied to it. After wiener filter, some noise in the image decreases but retinal image pixels soften. Therefore, Otsu thresholding is applied to softened images. Finally, morphological operation is performed on gray level images. The proposed method is implemented on test images in DRIVE database. The process time of our method is 0.7-0.8 second and it is faster than other methods. 95,61% accuracy, 85.096% sensitivity and 96.33% specificity rates are obtained.