title: Improved automatic centerline tracing for dendritic and axonal structures.
abstract: Centerline tracing in dendritic structures acquired from confocal images of neurons is an essential tool for the construction of geometrical representations of a neuronal network from its coarse scale up to its fine scale structures. In this paper, we propose an algorithm for centerline extraction that is both highly accurate and computationally efficient. The main novelties of the proposed method are (1) the use of a small set of Multiscale Isotropic Laplacian filters, acting as self-steerable filters, for a quick and efficient binary segmentation of dendritic arbors and axons; (2) an automated centerline seed points detection method based on the application of a simple 3D finite-length filter. The performance of this algorithm, which is validated on data from the DIADEM set appears to be very competitive when compared with other state-of-the-art algorithms.
authors.affiliation: Department of Mathematics, University of Costa Rica, San Pedro Montes de Oca, San José, Republic of Costa Rica, david.jimenezlopez@ucr.ac.cr.
authors.forename: David
authors.initials: D
authors.lastname: Jiménez
authors.forename: Demetrio
authors.initials: D
authors.lastname: Labate
authors.forename: Ioannis A
authors.initials: IA
authors.lastname: Kakadiaris
authors.forename: Manos
authors.initials: M
authors.lastname: Papadakis
mesh_terms: D000465:Algorithms; D000818:Animals; D001369:Axons / Q000502:physiology*; D003712:Dendrites / Q000502:physiology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008959:Models, Neurological; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated