A new supervised retinal vessel segmentation method based on robust hybrid features

In this paper, we propose a new supervised retinal blood vessel segmentation method that combines a set of very robust features from different algorithms into a hybrid feature vector for pixel characterization. This 17-D feature vector consists of 13 Gabor filter responses computed at different configurations, contrast enhanced intensity, morphological top-hat transformed intensity, vesselness measure, and B-COSFIRE filter response. A random forest classifier, known for its speed, simplicity, and information fusion capability, is trained with the hybrid feature vector. The chosen combination of the different types of individually strong features results in increased local information with better discrimination for vessel and non-vessel pixels in both healthy and pathological retinal images. The proposed method is evaluated in detail on two publicly available databases DRIVE and STARE. Average classification accuracies of 0.9513 and 0.9605 on the DRIVE and STARE datasets, respectively, are achieved. When the majority of the common performance metrics are considered, our method is superior to the state-of-the-art methods. Performance results show that our method also outperforms the state-of-the-art methods in both cross training and pathological cases.