title: Vessel network detection using contour evolution and color components.
abstract: Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration. An extensive related literature often excludes the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper consists in an algorithm using front propagation to segment the vessel network, including a penalty on the wait queue to the fast marching method, which minimizes leakage of the evolving boundary. The algorithm requires no manual labeling of seeds, a minimum number of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.
authors.affiliation: Lawrence Berkeley National Laboratory, CA, USA. dushizima@lbl.gov
authors.forename: Daniela M
authors.initials: DM
authors.lastname: Ushizima
authors.forename: Fatima N S
authors.initials: FN
authors.lastname: Medeiros
authors.forename: Jorge
authors.initials: J
authors.lastname: Cuadros
authors.forename: Charles I O
authors.initials: CI
authors.lastname: Martins
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D003930:Diabetic Retinopathy / Q000473:pathology*; 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