  In this study, a supervised retina blood vessel segmentation process was
performed on the green channel of the RGB image using artificial neural network
(ANN). The green channel is preferred because the retinal vessel structures can
be distinguished most clearly from the green channel of the RGB image. The
study was performed using 20 images in the DRIVE data set which is one of the
most common retina data sets known. The images went through some preprocessing
stages like contrastlimited adaptive histogram equalization (CLAHE), color
intensity adjustment, morphological operations and median and Gaussian
filtering to obtain a good segmentation. Retinal vessel structures were
highlighted with top-hat and bot-hat morphological operations and converted to
binary image by using global thresholding. Then, the network was trained by the
binary version of the images specified as training images in the dataset and
the targets are the images segmented manually by a specialist. The average
segmentation accuracy for 20 images was found as 0.9492.
