Hierarchical retinal blood vessel segmentation based on feature and ensemble learning

• A supervised method based on feature and ensemble learning is proposed.
• The whole pipeline of the proposed method is automatic and trainable.
• Convolutional Neural Network performs as a trainable hierarchical feature extractor.
• Ensemble Random Forests work as a trainable classifier.
• Compared with state-of-the-arts, the experimental results are promising.

Segmentation of retinal blood vessels is of substantial clinical importance for diagnoses of many diseases, such as diabetic retinopathy, hypertension and cardiovascular diseases. In this paper, the supervised method is presented to tackle the problem of retinal blood vessel segmentation, which combines two superior classifiers: Convolutional Neural Network (CNN) and Random Forest (RF). In this method, CNN performs as a trainable hierarchical feature extractor and ensemble RFs work as a trainable classifier. By integrating the merits of feature learning and traditional classifier, the proposed method is able to automatically learn features from the raw images and predict the patterns. Extensive experiments have been conducted on two public retinal images databases (DRIVE and STARE), and comparisons with other major studies on the same database demonstrate the promising performance and effectiveness of the proposed method.