FABC: Retinal Vessel Segmentation Using AdaBoost

摘要

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on $789, 914$ gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of $40$ manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the $20$ images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of $0.9561$, in line with state-of-the-art approaches, but outperforming their accuracy ($0.9597$ versus $0.9473$ for the nearest performer).