FABC: retinal vessel segmentation using AdaBoost

Carmen Alina Lupascu, Domenico Tegolo, Emanuele Trucco

    Research output: Contribution to journalArticle

    232 Citations (Scopus)

    Abstract

    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 Ada Boost 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 Ada Boost 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).

    Original languageEnglish
    Pages (from-to)1267-1274
    Number of pages8
    JournalIEEE Transactions on Information Technology in Biomedicine
    Volume14
    Issue number5
    DOIs
    Publication statusPublished - 2010

    Keywords

    • AdaBoost classifier
    • retinal images
    • vessel segmentation
    • BLOOD-VESSELS
    • FUNDUS IMAGES
    • EDGE-DETECTION
    • MATCHED-FILTERS
    • OPTIC DISC
    • MODEL
    • EXTRACTION
    • NETWORK

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