Retinal Vessel Segmentation in Fundus Images Using CART and AdaBoost

It is proposed an effective method based on supervised learning for retinal vessel segmentation in fundus images.To determine whether a pixel is in the vessel, a 39-dimensional feature vector is extracted for every pixel, consisting of local features, morphological features and Gabor features.Afterwards, the sampled set is first treated by the classification and regression tree (CART) as a weak classifier, and then strengthened by a trained AdaBoost-based classifier as a strong classifier, to classify the pixels.The proposed method is evaluated with the public digital retinal images for vessel extraction (DRIVE) set and experimental results show that the proposed method has a high average accuracy of 0.9607and performs better than other approaches based on supervised learning in sensitivity and specificity.It is suitable for computer-aided eye disease diagnosis and evaluation using fundus images.