title: Reconstructing geometrically consistent tree structures from noisy images.
abstract: We present a novel approach to fully automated reconstruction of tree structures in noisy 2D images. Unlike in earlier approaches, we explicitly handle crossovers and bifurcation points, and impose geometric constraints while optimizing a global cost function. We use manually annotated retinal scans to evaluate our method and demonstrate that it brings about a very substantial improvement.
authors.affiliation: Computer Vision Lab., Ecole Polytechnique Fédérale de Lausanne, Switzerland.
authors.forename: Engin
authors.initials: E
authors.lastname: Türetken
authors.forename: Christian
authors.initials: C
authors.lastname: Blum
authors.forename: Germán
authors.initials: G
authors.lastname: González
authors.forename: Pascal
authors.initials: P
authors.lastname: Fua
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity