title: Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model.
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.
authors.affiliation: VAMPIRE Project, Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, Italy. Electronic address: lupascu@math.unipa.it.
authors.forename: Carmen Alina
authors.initials: CA
authors.lastname: Lupaşcu
authors.forename: Domenico
authors.initials: D
authors.lastname: Tegolo
authors.forename: Emanuele
authors.initials: E
authors.lastname: Trucco
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000473:pathology*; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Ensembles of bagged decision trees; Medical image analysis; Multiresolution Hermite model; Retinal images; Vessel width