Blood vessel segmentation in eye fundus images

摘要

This paper proposes a convolutional neural network architecture for blood vessel segmentation in retinal images. The network structure is designed on 7 layers using MatConvNet (three convolutional layers, two pooling layers, one dropout layer and a Softmax layer). The input data, selected from the DRIVE database, of the neural network is preprocessed in Matlab on Green channel. The retinal image was partitioned in patches of dimension 27 × 27 pixels using a sliding box algorithm. The whole network was trained using a number of 400,000 patches. The CNN was implemented using GPU Programming in MATLAB. The results are promising taking into account speed of processing and the simplicity of the network.