Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model

Carmen Alina Lupaşcu, Domenico Tegolo, Emanuele Trucco

    Research output: Contribution to journalArticlepeer-review

    36 Citations (Scopus)

    Abstract

    We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface.We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests.An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy.
    Original languageEnglish
    Pages (from-to)1164-1180
    Number of pages17
    JournalMedical Image Analysis
    Volume17
    Issue number8
    DOIs
    Publication statusPublished - 1 Dec 2013

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