Retinal vessel segmentation method based on improved U-NET network

Abstract

The histomorphology of retina is closely related to some common human diseases, such as glaucoma, macular degeneration. The use of deep learning-assisted diagnosis reduces the rate of misdiagnosis and early screening of diseases. There are several difficulties in retinal vessel segmentation as follows. Small vessels located at the end of branches are difficult to be discerned by human eyes. Camera illumination is insufficient or overexposed, resulting in too bright optic disc area, low contrast and blurred retinal vessel side inspection. The unique tree bifurcation structure of retinal vessels is difficult to maintain the original appearance of the structure because the vessels are too thin to be detected. In this paper, we use a U-net network with a stacked full convolutional structure to achieve accurate segmentation of retinal vessels. The main work is as follows. Firstly, the original data are preprocessed: the database images are RGB images, and in order to improve the segmentation accuracy, the channels are extracted for preprocessing first. Secondly, CLAHE is performed to enhance the contrast of the vascular region. Finally, the data is fed into the network for training. U-net is a modified network model of FCN, which mainly consists of feature extraction and upsampling. The feature extraction is used to capture the contextual information in the image, and the upsampling part is used to recover the location information of the image. Compared with the existing algorithms, the proposed algorithm can segment retinal vessels more effectively its sensitivity and accuracy have been significantly improved.