Retinal blood vessel segmentation based on the Gaussian matched filter and U-net

Abstract

The automatic segmentation of retinal vessels plays an important role in the early screening of eye diseases. However, pathological retinal images are difficult for us to segment the vessels. In this paper, we regard the vessels segmentation task as a multi-label problem and combine the preprocessed method Gaussian matched filter with a new U-shaped fully convolutional neural network called U-net to generate a blood vessels segmentation framework. The output of this model can distinguish the vessels from background although in the inadequate contrast regions and pathological regions. The proposed method is tested on a publicly available dataset of DRIVE. Sensitivity, Specificity, Accuracy and Precision are used to evaluate our method, and the average classification accuracy is 0.9636 on the dataset of DRIVE. Performance results show that our method outperforms the state-of-the-art method for automatic retinal blood segmentation.