Convolutional Neural Network for Retinal Blood Vessel Segmentation

Abstract

This paper proposes a CNN (Convolutional neural network) based blood vessel segmentation algorithm. Each pixel with its neighbors of the fundus image is checked by the CNN. The preliminary segmentation results of fundus images were refined by a two stages binarization and a morphological operation successively. The algorithm was tested on DRIVE dataset. While the specificity is 0.9603, sensitivity is 0.7731, which is very close to that of manual annotation. The sensitivity is 2% better than the ones found in current studies. The CNN based algorithm improves the segmentation of blood vessels performance significantly.