Retinal Blood Vessel Segmentation with Improved Convolutional Neural Networks

Retinal blood vessel feature is one of crucial biomarkers for ophthalmologic and cardiovascular diseases, efficiency image segmentation technologies will help doctors diagnose these related diseases. We propose an improved deep CNN model to segment retinal blood vessels. Our method includes three steps: Data augmentation, Image preprocessing methods and Model training. The data augmentation uses the rotation and image mirroring to make the training image better generalization. The CLAHE algorithm is used for image preprocessing, which can reduce the image noise and enhance tiny retinal blood vessels features. Finally, we used a deep CNN model combined with U-Net and Dense-Net structure to train retinal blood vessel image. The result of proposed model was tested on public available dataset DRIVE, achieving an average accuracy 0.951, specificity 0.973, sensitivity 0.797 and the average AUC is 0.885. The results show its potential for clinical application.